mimic_prediction_arima/analysis_arima-x_accuracy.ipynb

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 "cells": [
  {
   "source": [
    "# Analysis of ARIMA(X) Forecasts\n",
    "\n",
    "## Load Data"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# path_to_data = '/hpi/fs00/share/MPSS2021BA1/data/'\n",
    "path_to_data = '../data/arima-results_2021-07-05/'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 01 - ARIMA - Steady Train Size 12\n",
    "import pickle\n",
    "# Heart Rate\n",
    "input_file = open(path_to_data+'accuracy_dict_for_chunk_iterations_arima_12_hr_first1000.pickle', 'rb')\n",
    "accuracy_dict_for_chunk_iterations_arima_12_hr = pickle.load(input_file)\n",
    "input_file.close()\n",
    "# Blood Pressure\n",
    "input_file = open(path_to_data+'accuracy_dict_for_chunk_iterations_arima_12_bp_first1000.pickle', 'rb')\n",
    "accuracy_dict_for_chunk_iterations_arima_12_bp = pickle.load(input_file)\n",
    "input_file.close()\n",
    "# O2 Saturation\n",
    "input_file = open(path_to_data+'accuracy_dict_for_chunk_iterations_arima_12_o2_first1000.pickle', 'rb')\n",
    "accuracy_dict_for_chunk_iterations_arima_12_o2 = pickle.load(input_file)\n",
    "input_file.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 01 - ARIMA - Steady Train Size 30\n",
    "import pickle\n",
    "# Heart Rate\n",
    "input_file = open(path_to_data+'accuracy_dict_for_chunk_iterations_arima_30_hr_first1000.pickle', 'rb')\n",
    "accuracy_dict_for_chunk_iterations_arima_30_hr = pickle.load(input_file)\n",
    "input_file.close()\n",
    "# Blood Pressure\n",
    "input_file = open(path_to_data+'accuracy_dict_for_chunk_iterations_arima_30_bp_first1000.pickle', 'rb')\n",
    "accuracy_dict_for_chunk_iterations_arima_30_bp = pickle.load(input_file)\n",
    "input_file.close()\n",
    "# O2 Saturation\n",
    "input_file = open(path_to_data+'accuracy_dict_for_chunk_iterations_arima_30_o2_first1000.pickle', 'rb')\n",
    "accuracy_dict_for_chunk_iterations_arima_30_o2 = pickle.load(input_file)\n",
    "input_file.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 02 - ARIMA - Expanding Train Size 12\n",
    "import pickle\n",
    "# Heart Rate\n",
    "input_file = open(path_to_data+'accuracy_dict_for_chunk_iterations_arima_expanding_12_hr_first1000.pickle', 'rb')\n",
    "accuracy_dict_for_chunk_iterations_arima_expanding_12_hr = pickle.load(input_file)\n",
    "input_file.close()\n",
    "# Blood Pressure\n",
    "input_file = open(path_to_data+'accuracy_dict_for_chunk_iterations_arima_expanding_12_bp_first1000.pickle', 'rb')\n",
    "accuracy_dict_for_chunk_iterations_arima_expanding_12_bp = pickle.load(input_file)\n",
    "input_file.close()\n",
    "# O2 Saturation\n",
    "input_file = open(path_to_data+'accuracy_dict_for_chunk_iterations_arima_expanding_12_o2_first1000.pickle', 'rb')\n",
    "accuracy_dict_for_chunk_iterations_arima_expanding_12_o2 = pickle.load(input_file)\n",
    "input_file.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 02 - ARIMA - Expanding Train Size 30\n",
    "import pickle\n",
    "# Heart Rate\n",
    "input_file = open(path_to_data+'accuracy_dict_for_chunk_iterations_arima_expanding_30_hr_first1000.pickle', 'rb')\n",
    "accuracy_dict_for_chunk_iterations_arima_expanding_30_hr = pickle.load(input_file)\n",
    "input_file.close()\n",
    "# Blood Pressure\n",
    "input_file = open(path_to_data+'accuracy_dict_for_chunk_iterations_arima_expanding_30_bp_first1000.pickle', 'rb')\n",
    "accuracy_dict_for_chunk_iterations_arima_expanding_30_bp = pickle.load(input_file)\n",
    "input_file.close()\n",
    "# O2 Saturation\n",
    "input_file = open(path_to_data+'accuracy_dict_for_chunk_iterations_arima_expanding_30_o2_first1000.pickle', 'rb')\n",
    "accuracy_dict_for_chunk_iterations_arima_expanding_30_o2 = pickle.load(input_file)\n",
    "input_file.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 03 - ARIMAX - Steady Train Size 12\n",
    "import pickle\n",
    "# Heart Rate\n",
    "input_file = open(path_to_data+'accuracy_dict_for_chunk_iterations_arimax_12_hr_first1000.pickle', 'rb')\n",
    "accuracy_dict_for_chunk_iterations_arimax_12_hr = pickle.load(input_file)\n",
    "input_file.close()\n",
    "# Blood Pressure\n",
    "input_file = open(path_to_data+'accuracy_dict_for_chunk_iterations_arimax_12_bp_first1000.pickle', 'rb')\n",
    "accuracy_dict_for_chunk_iterations_arimax_12_bp = pickle.load(input_file)\n",
    "input_file.close()\n",
    "# O2 Saturation\n",
    "input_file = open(path_to_data+'accuracy_dict_for_chunk_iterations_arimax_12_o2_first1000.pickle', 'rb')\n",
    "accuracy_dict_for_chunk_iterations_arimax_12_o2 = pickle.load(input_file)\n",
    "input_file.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 03 - ARIMAX - Steady Train Size 30\n",
    "import pickle\n",
    "# Heart Rate\n",
    "input_file = open(path_to_data+'accuracy_dict_for_chunk_iterations_arimax_30_hr_first1000.pickle', 'rb')\n",
    "accuracy_dict_for_chunk_iterations_arimax_30_hr = pickle.load(input_file)\n",
    "input_file.close()\n",
    "# Blood Pressure\n",
    "input_file = open(path_to_data+'accuracy_dict_for_chunk_iterations_arimax_30_bp_first1000.pickle', 'rb')\n",
    "accuracy_dict_for_chunk_iterations_arimax_30_bp = pickle.load(input_file)\n",
    "input_file.close()\n",
    "# O2 Saturation\n",
    "input_file = open(path_to_data+'accuracy_dict_for_chunk_iterations_arimax_30_o2_first1000.pickle', 'rb')\n",
    "accuracy_dict_for_chunk_iterations_arimax_30_o2 = pickle.load(input_file)\n",
    "input_file.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 04 - ARIMAX - Expanding Train Size 12\n",
    "import pickle\n",
    "# Heart Rate\n",
    "input_file = open(path_to_data+'accuracy_dict_for_chunk_iterations_arimax_expanding_12_hr_first1000.pickle', 'rb')\n",
    "accuracy_dict_for_chunk_iterations_arimax_expanding_12_hr = pickle.load(input_file)\n",
    "input_file.close()\n",
    "# Blood Pressure\n",
    "input_file = open(path_to_data+'accuracy_dict_for_chunk_iterations_arimax_expanding_12_bp_first1000.pickle', 'rb')\n",
    "accuracy_dict_for_chunk_iterations_arimax_expanding_12_bp = pickle.load(input_file)\n",
    "input_file.close()\n",
    "# O2 Saturation\n",
    "input_file = open(path_to_data+'accuracy_dict_for_chunk_iterations_arimax_expanding_12_o2_first1000.pickle', 'rb')\n",
    "accuracy_dict_for_chunk_iterations_arimax_expanding_12_o2 = pickle.load(input_file)\n",
    "input_file.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 04 - ARIMAX - Expanding Train Size 30\n",
    "import pickle\n",
    "# Heart Rate\n",
    "input_file = open(path_to_data+'accuracy_dict_for_chunk_iterations_arimax_expanding_30_hr_first1000.pickle', 'rb')\n",
    "accuracy_dict_for_chunk_iterations_arimax_expanding_30_hr = pickle.load(input_file)\n",
    "input_file.close()\n",
    "# Blood Pressure\n",
    "input_file = open(path_to_data+'accuracy_dict_for_chunk_iterations_arimax_expanding_30_bp_first1000.pickle', 'rb')\n",
    "accuracy_dict_for_chunk_iterations_arimax_expanding_30_bp = pickle.load(input_file)\n",
    "input_file.close()\n",
    "# O2 Saturation\n",
    "input_file = open(path_to_data+'accuracy_dict_for_chunk_iterations_arimax_expanding_30_o2_first1000.pickle', 'rb')\n",
    "accuracy_dict_for_chunk_iterations_arimax_expanding_30_o2 = pickle.load(input_file)\n",
    "input_file.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 05 - Darts ARIMA - Steady Train Size 30\n",
    "import pickle\n",
    "# Heart Rate\n",
    "input_file = open(path_to_data+'accuracy_dict_for_chunk_iterations_darts_arima_30_hr_first1000.pickle', 'rb')\n",
    "accuracy_dict_for_chunk_iterations_darts_arima_30_hr = pickle.load(input_file)\n",
    "input_file.close()\n",
    "# Blood Pressure\n",
    "input_file = open(path_to_data+'accuracy_dict_for_chunk_iterations_darts_arima_30_bp_first1000.pickle', 'rb')\n",
    "accuracy_dict_for_chunk_iterations_darts_arima_30_bp = pickle.load(input_file)\n",
    "input_file.close()\n",
    "# O2 Saturation\n",
    "input_file = open(path_to_data+'accuracy_dict_for_chunk_iterations_darts_arima_30_o2_first1000.pickle', 'rb')\n",
    "accuracy_dict_for_chunk_iterations_darts_arima_30_o2 = pickle.load(input_file)\n",
    "input_file.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 06 - Darts ARIMA - Expanding Train Size 30\n",
    "import pickle\n",
    "# Heart Rate\n",
    "input_file = open(path_to_data+'accuracy_dict_for_chunk_iterations_darts_arima_expanding_30_hr_first1000.pickle', 'rb')\n",
    "accuracy_dict_for_chunk_iterations_darts_arima_expanding_30_hr = pickle.load(input_file)\n",
    "input_file.close()\n",
    "# Blood Pressure\n",
    "input_file = open(path_to_data+'accuracy_dict_for_chunk_iterations_darts_arima_expanding_30_bp_first1000.pickle', 'rb')\n",
    "accuracy_dict_for_chunk_iterations_darts_arima_expanding_30_bp = pickle.load(input_file)\n",
    "input_file.close()\n",
    "# O2 Saturation\n",
    "input_file = open(path_to_data+'accuracy_dict_for_chunk_iterations_darts_arima_expanding_30_o2_first1000.pickle', 'rb')\n",
    "accuracy_dict_for_chunk_iterations_darts_arima_expanding_30_o2 = pickle.load(input_file)\n",
    "input_file.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 07 - Darts ARIMAX - Steady Train Size 30\n",
    "import pickle\n",
    "# Heart Rate\n",
    "input_file = open(path_to_data+'accuracy_dict_for_chunk_iterations_darts_arimax_30_hr_first1000.pickle', 'rb')\n",
    "accuracy_dict_for_chunk_iterations_darts_arimax_30_hr = pickle.load(input_file)\n",
    "input_file.close()\n",
    "# Blood Pressure\n",
    "input_file = open(path_to_data+'accuracy_dict_for_chunk_iterations_darts_arimax_30_bp_first1000.pickle', 'rb')\n",
    "accuracy_dict_for_chunk_iterations_darts_arimax_30_bp = pickle.load(input_file)\n",
    "input_file.close()\n",
    "# O2 Saturation\n",
    "input_file = open(path_to_data+'accuracy_dict_for_chunk_iterations_darts_arimax_30_o2_first1000.pickle', 'rb')\n",
    "accuracy_dict_for_chunk_iterations_darts_arimax_30_o2 = pickle.load(input_file)\n",
    "input_file.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 08 - Darts ARIMAX - Expanding Train Size 30\n",
    "import pickle\n",
    "# Heart Rate\n",
    "input_file = open(path_to_data+'accuracy_dict_for_chunk_iterations_darts_arimax_expanding_30_hr_first1000.pickle', 'rb')\n",
    "accuracy_dict_for_chunk_iterations_darts_arimax_expanding_30_hr = pickle.load(input_file)\n",
    "input_file.close()\n",
    "# Blood Pressure\n",
    "input_file = open(path_to_data+'accuracy_dict_for_chunk_iterations_darts_arimax_expanding_30_bp_first1000.pickle', 'rb')\n",
    "accuracy_dict_for_chunk_iterations_darts_arimax_expanding_30_bp = pickle.load(input_file)\n",
    "input_file.close()\n",
    "# O2 Saturation\n",
    "input_file = open(path_to_data+'accuracy_dict_for_chunk_iterations_darts_arimax_expanding_30_o2_first1000.pickle', 'rb')\n",
    "accuracy_dict_for_chunk_iterations_darts_arimax_expanding_30_o2 = pickle.load(input_file)\n",
    "input_file.close()"
   ]
  },
  {
   "source": [
    "## Prepare Data"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import time\n",
    "\n",
    "# Transform nested dictionary into data frame using pd.concat()\n",
    "def dict_to_df(selected_dict):\n",
    "\n",
    "    starttime = time.time()\n",
    "    print('START transforming dictionary.')\n",
    "    chunkno = 0\n",
    "\n",
    "    selected_dict_as_df = pd.DataFrame({})\n",
    "    confusion_matrices_per_chunk_in_selected_dict = {}\n",
    "    for chunkid in selected_dict:\n",
    "        confusion_matrices_per_chunk_in_selected_dict[chunkid] = pd.concat(selected_dict[chunkid], axis=0).reset_index().rename(columns={'level_0':'ITERATION', 'level_1':'ACCURACY_TYPE'})\n",
    "\n",
    "        chunkno = chunkno+1\n",
    "        # runningtime = round(((time.time() - starttime) / 60), 5)\n",
    "        # print('Completed chunk '+str(chunkid)+', running time in minutes: '+str(runningtime))\n",
    "\n",
    "    selected_dict_as_df = pd.concat(confusion_matrices_per_chunk_in_selected_dict, axis=0).reset_index(level=0).rename(columns={'level_0':'CHUNK_ID_FILLED_TH'})\n",
    "\n",
    "    endtime = round(((time.time() - starttime) / 60), 5)\n",
    "    print('DONE transforming dictionary.')\n",
    "    print('Completed '+str(chunkno)+' chunks in '+str(endtime)+' minutes')\n",
    "    print('Number of chunk-iteration combinations:',sum(selected_dict_as_df.groupby(['CHUNK_ID_FILLED_TH'])['ITERATION'].nunique()))\n",
    "    print('--------------------')\n",
    "\n",
    "    return selected_dict_as_df\n",
    "\n",
    "# Prepare data frame to look pretty (rename column, replace values)\n",
    "def prepare_df(df):\n",
    "    df.rename(columns = {'CHUNK_ID_FILLED_TH':'CHUNKID', 'ACCURACY_TYPE':'ALARMTYPE'}, inplace=True)\n",
    "    df.replace(\n",
    "        ['accuracy_high_alarms_arima', 'accuracy_low_alarms_arima','accuracy_high_alarms_arimax', 'accuracy_low_alarms_arimax'],\n",
    "        ['High', 'Low','High', 'Low'],\n",
    "        inplace=True)\n",
    "    return df\n",
    "\n",
    "# Create confusion matrix per chunk by aggregating so that TP, FN, FP, and TN of the several chunk iterations are summed up\n",
    "def aggregate_to_confusion_matrix_per_chunk(df):\n",
    "    confusion_matrix_per_chunk = pd.DataFrame({})\n",
    "    confusion_matrix_per_chunk = df.groupby(['CHUNKID', 'ACCURACY_TYPE'])[['TP', 'FN', 'FP', 'TN']].agg('sum').reset_index()\n",
    "    return confusion_matrix_per_chunk\n",
    "\n",
    "# Create confusion matrix by aggregating so that TP, FN, FP, and TN of all chunks are summed up\n",
    "def aggregate_to_confusion_matrix_per_model(df):\n",
    "    confusion_matrix_per_model = df.groupby(['ALARMTYPE'])[['TP', 'FN', 'FP', 'TN']].agg('sum')\n",
    "    return confusion_matrix_per_model\n",
    "\n",
    "# Calculate statistics about the number of chunks as well as chunk iteration combinations \n",
    "def calc_stats(df):\n",
    "    chunks = len(df.CHUNKID.unique())\n",
    "    iterations = sum(df.groupby(['CHUNKID'])['ITERATION'].nunique())\n",
    "    stats = pd.DataFrame({\n",
    "        'CHUNKS':       [chunks],\n",
    "        'ITERATIONS':   [iterations]\n",
    "        })\n",
    "    return stats\n",
    "\n",
    "# Create confusion matrix per chunk by aggregating so that TP, FN, FP, and TN of the several chunk iterations are summed up\n",
    "def aggregate_to_confusion_matrix_per_chunk(df):\n",
    "    confusion_matrix_per_chunk = df.groupby(['CHUNKID', 'ALARMTYPE'])[['TP', 'FN', 'FP', 'TN']].agg('sum').reset_index()\n",
    "    return confusion_matrix_per_chunk\n",
    "\n",
    "# Create confusion matrix by aggregating so that TP, FN, FP, and TN of all chunks are summed up\n",
    "def aggregate_to_confusion_matrix_per_model(df):\n",
    "    confusion_matrix_per_model = df.groupby(['ALARMTYPE'])[['TP', 'FN', 'FP', 'TN']].agg('sum')\n",
    "    return confusion_matrix_per_model\n",
    "\n",
    "# Calculate statistical measures for evaluation of the model\n",
    "def calc_metrics(df):\n",
    "    TP_high = df.iloc[0,0]\n",
    "    FN_high = df.iloc[0,1]\n",
    "    FP_high = df.iloc[0,2]\n",
    "    TN_high = df.iloc[0,3]\n",
    "\n",
    "    TP_low = df.iloc[1,0]\n",
    "    FN_low = df.iloc[1,1]\n",
    "    FP_low = df.iloc[1,2]\n",
    "    TN_low = df.iloc[1,3]\n",
    "\n",
    "    fpr_high = FP_high/(FP_high+TN_high)\n",
    "    tpr_high = TP_high/(TP_high+FN_high)\n",
    "    fnr_high = FN_high/(TP_high+FN_high)\n",
    "    tnr_high = TN_high/(FP_high+TN_high)\n",
    "    acc_high = (TP_high+TN_high)/(TP_high+FN_high+FP_high+TN_high)\n",
    "    f1s_high = TP_high/(TP_high+0.5*(FP_high+FN_high))\n",
    "\n",
    "    fpr_low = FP_low/(FP_low+TN_low)\n",
    "    tpr_low = TP_low/(TP_low+FN_low)\n",
    "    fnr_low = FN_low/(TP_low+FN_low)\n",
    "    tnr_low = TN_low/(FP_low+TN_low)\n",
    "    acc_low = (TP_low+TN_low)/(TP_low+FN_low+FP_low+TN_low)\n",
    "    f1s_low = TP_low/(TP_low+0.5*(FP_low+FN_low))\n",
    "\n",
    "    metrics = pd.DataFrame({\n",
    "        'ALARMTYPE':    ['High',        'Low'],\n",
    "        'FPR':          [fpr_high,      fpr_low],\n",
    "        'TPR':          [tpr_high,      tpr_low],\n",
    "        'FNR':          [fnr_high,      fnr_low],\n",
    "        'TNR':          [tnr_high,      tnr_low],\n",
    "        'ACC':          [acc_high,      acc_low],\n",
    "        'F1S':          [f1s_high,      f1s_low]\n",
    "        }).round(4)\n",
    "    \n",
    "    return metrics\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "START transforming dictionary.\n",
      "DONE transforming dictionary.\n",
      "Completed 671 chunks in 0.15776 minutes\n",
      "Number of chunk-iteration combinations: 25111\n",
      "--------------------\n",
      "START transforming dictionary.\n",
      "DONE transforming dictionary.\n",
      "Completed 207 chunks in 0.02515 minutes\n",
      "Number of chunk-iteration combinations: 2407\n",
      "--------------------\n",
      "START transforming dictionary.\n",
      "DONE transforming dictionary.\n",
      "Completed 438 chunks in 0.08042 minutes\n",
      "Number of chunk-iteration combinations: 11715\n",
      "--------------------\n"
     ]
    }
   ],
   "source": [
    "# 01 - ARIMA - Steady Train Size 12\n",
    "# Transform nested dictionary into data frame\n",
    "\n",
    "confusion_matrices_per_chunk_01_arima_steady_12_hr = dict_to_df(accuracy_dict_for_chunk_iterations_arima_12_hr)\n",
    "confusion_matrices_per_chunk_01_arima_steady_12_hr = prepare_df(confusion_matrices_per_chunk_01_arima_steady_12_hr)\n",
    "\n",
    "confusion_matrices_per_chunk_01_arima_steady_12_bp = dict_to_df(accuracy_dict_for_chunk_iterations_arima_12_bp)\n",
    "confusion_matrices_per_chunk_01_arima_steady_12_bp = prepare_df(confusion_matrices_per_chunk_01_arima_steady_12_bp)\n",
    "\n",
    "confusion_matrices_per_chunk_01_arima_steady_12_o2 = dict_to_df(accuracy_dict_for_chunk_iterations_arima_12_o2)\n",
    "confusion_matrices_per_chunk_01_arima_steady_12_o2 = prepare_df(confusion_matrices_per_chunk_01_arima_steady_12_o2)\n",
    "\n",
    "# Calculate statistics about the number of chunks as well as chunk iteration combinations\n",
    "\n",
    "stats_01_arima_steady_12_hr = calc_stats(confusion_matrices_per_chunk_01_arima_steady_12_hr)\n",
    "\n",
    "stats_01_arima_steady_12_bp = calc_stats(confusion_matrices_per_chunk_01_arima_steady_12_bp)\n",
    "\n",
    "stats_01_arima_steady_12_o2 = calc_stats(confusion_matrices_per_chunk_01_arima_steady_12_o2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "START transforming dictionary.\n",
      "DONE transforming dictionary.\n",
      "Completed 374 chunks in 0.09315 minutes\n",
      "Number of chunk-iteration combinations: 15766\n",
      "--------------------\n",
      "START transforming dictionary.\n",
      "DONE transforming dictionary.\n",
      "Completed 45 chunks in 0.00578 minutes\n",
      "Number of chunk-iteration combinations: 580\n",
      "--------------------\n",
      "START transforming dictionary.\n",
      "DONE transforming dictionary.\n",
      "Completed 188 chunks in 0.03616 minutes\n",
      "Number of chunk-iteration combinations: 6246\n",
      "--------------------\n"
     ]
    }
   ],
   "source": [
    "# 01 - ARIMA - Steady Train Size 30\n",
    "# Transform nested dictionary into data frame\n",
    "\n",
    "confusion_matrices_per_chunk_01_arima_steady_30_hr = dict_to_df(accuracy_dict_for_chunk_iterations_arima_30_hr)\n",
    "confusion_matrices_per_chunk_01_arima_steady_30_hr = prepare_df(confusion_matrices_per_chunk_01_arima_steady_30_hr)\n",
    "\n",
    "confusion_matrices_per_chunk_01_arima_steady_30_bp = dict_to_df(accuracy_dict_for_chunk_iterations_arima_30_bp)\n",
    "confusion_matrices_per_chunk_01_arima_steady_30_bp = prepare_df(confusion_matrices_per_chunk_01_arima_steady_30_bp)\n",
    "\n",
    "confusion_matrices_per_chunk_01_arima_steady_30_o2 = dict_to_df(accuracy_dict_for_chunk_iterations_arima_30_o2)\n",
    "confusion_matrices_per_chunk_01_arima_steady_30_o2 = prepare_df(confusion_matrices_per_chunk_01_arima_steady_30_o2)\n",
    "\n",
    "# Calculate statistics about the number of chunks as well as chunk iteration combinations\n",
    "\n",
    "stats_01_arima_steady_30_hr = calc_stats(confusion_matrices_per_chunk_01_arima_steady_30_hr)\n",
    "\n",
    "stats_01_arima_steady_30_bp = calc_stats(confusion_matrices_per_chunk_01_arima_steady_30_bp)\n",
    "\n",
    "stats_01_arima_steady_30_o2 = calc_stats(confusion_matrices_per_chunk_01_arima_steady_30_o2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "START transforming dictionary.\n",
      "DONE transforming dictionary.\n",
      "Completed 671 chunks in 0.15902 minutes\n",
      "Number of chunk-iteration combinations: 25111\n",
      "--------------------\n",
      "START transforming dictionary.\n",
      "DONE transforming dictionary.\n",
      "Completed 207 chunks in 0.02637 minutes\n",
      "Number of chunk-iteration combinations: 2406\n",
      "--------------------\n",
      "START transforming dictionary.\n",
      "DONE transforming dictionary.\n",
      "Completed 438 chunks in 0.0782 minutes\n",
      "Number of chunk-iteration combinations: 11716\n",
      "--------------------\n"
     ]
    }
   ],
   "source": [
    "# 02 - ARIMA - Expanding Train Size 12\n",
    "# Transform nested dictionary into data frame\n",
    "\n",
    "confusion_matrices_per_chunk_02_arima_expanding_12_hr = dict_to_df(accuracy_dict_for_chunk_iterations_arima_expanding_12_hr)\n",
    "confusion_matrices_per_chunk_02_arima_expanding_12_hr = prepare_df(confusion_matrices_per_chunk_02_arima_expanding_12_hr)\n",
    "\n",
    "confusion_matrices_per_chunk_02_arima_expanding_12_bp = dict_to_df(accuracy_dict_for_chunk_iterations_arima_expanding_12_bp)\n",
    "confusion_matrices_per_chunk_02_arima_expanding_12_bp = prepare_df(confusion_matrices_per_chunk_02_arima_expanding_12_bp)\n",
    "\n",
    "confusion_matrices_per_chunk_02_arima_expanding_12_o2 = dict_to_df(accuracy_dict_for_chunk_iterations_arima_expanding_12_o2)\n",
    "confusion_matrices_per_chunk_02_arima_expanding_12_o2 = prepare_df(confusion_matrices_per_chunk_02_arima_expanding_12_o2)\n",
    "\n",
    "# Calculate statistics about the number of chunks as well as chunk iteration combinations\n",
    "\n",
    "stats_02_arima_expanding_12_hr = calc_stats(confusion_matrices_per_chunk_02_arima_expanding_12_hr)\n",
    "\n",
    "stats_02_arima_expanding_12_bp = calc_stats(confusion_matrices_per_chunk_02_arima_expanding_12_bp)\n",
    "\n",
    "stats_02_arima_expanding_12_o2 = calc_stats(confusion_matrices_per_chunk_02_arima_expanding_12_o2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "START transforming dictionary.\n",
      "DONE transforming dictionary.\n",
      "Completed 374 chunks in 0.10379 minutes\n",
      "Number of chunk-iteration combinations: 15766\n",
      "--------------------\n",
      "START transforming dictionary.\n",
      "DONE transforming dictionary.\n",
      "Completed 45 chunks in 0.00615 minutes\n",
      "Number of chunk-iteration combinations: 580\n",
      "--------------------\n",
      "START transforming dictionary.\n",
      "DONE transforming dictionary.\n",
      "Completed 188 chunks in 0.04011 minutes\n",
      "Number of chunk-iteration combinations: 6245\n",
      "--------------------\n"
     ]
    }
   ],
   "source": [
    "# 02 - ARIMA - Expanding Train Size 30\n",
    "# Transform nested dictionary into data frame\n",
    "\n",
    "confusion_matrices_per_chunk_02_arima_expanding_30_hr = dict_to_df(accuracy_dict_for_chunk_iterations_arima_expanding_30_hr)\n",
    "confusion_matrices_per_chunk_02_arima_expanding_30_hr = prepare_df(confusion_matrices_per_chunk_02_arima_expanding_30_hr)\n",
    "\n",
    "confusion_matrices_per_chunk_02_arima_expanding_30_bp = dict_to_df(accuracy_dict_for_chunk_iterations_arima_expanding_30_bp)\n",
    "confusion_matrices_per_chunk_02_arima_expanding_30_bp = prepare_df(confusion_matrices_per_chunk_02_arima_expanding_30_bp)\n",
    "\n",
    "confusion_matrices_per_chunk_02_arima_expanding_30_o2 = dict_to_df(accuracy_dict_for_chunk_iterations_arima_expanding_30_o2)\n",
    "confusion_matrices_per_chunk_02_arima_expanding_30_o2 = prepare_df(confusion_matrices_per_chunk_02_arima_expanding_30_o2)\n",
    "\n",
    "# Calculate statistics about the number of chunks as well as chunk iteration combinations\n",
    "\n",
    "stats_02_arima_expanding_30_hr = calc_stats(confusion_matrices_per_chunk_02_arima_expanding_30_hr)\n",
    "\n",
    "stats_02_arima_expanding_30_bp = calc_stats(confusion_matrices_per_chunk_02_arima_expanding_30_bp)\n",
    "\n",
    "stats_02_arima_expanding_30_o2 = calc_stats(confusion_matrices_per_chunk_02_arima_expanding_30_o2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "START transforming dictionary.\n",
      "DONE transforming dictionary.\n",
      "Completed 671 chunks in 0.15374 minutes\n",
      "Number of chunk-iteration combinations: 25112\n",
      "--------------------\n",
      "START transforming dictionary.\n",
      "DONE transforming dictionary.\n",
      "Completed 207 chunks in 0.02934 minutes\n",
      "Number of chunk-iteration combinations: 2407\n",
      "--------------------\n",
      "START transforming dictionary.\n",
      "DONE transforming dictionary.\n",
      "Completed 438 chunks in 0.08079 minutes\n",
      "Number of chunk-iteration combinations: 11717\n",
      "--------------------\n"
     ]
    }
   ],
   "source": [
    "# 03 - ARIMAX - Steady Train Size 12\n",
    "# Transform nested dictionary into data frame\n",
    "\n",
    "confusion_matrices_per_chunk_03_arimax_steady_12_hr = dict_to_df(accuracy_dict_for_chunk_iterations_arimax_12_hr)\n",
    "confusion_matrices_per_chunk_03_arimax_steady_12_hr = prepare_df(confusion_matrices_per_chunk_03_arimax_steady_12_hr)\n",
    "\n",
    "confusion_matrices_per_chunk_03_arimax_steady_12_bp = dict_to_df(accuracy_dict_for_chunk_iterations_arimax_12_bp)\n",
    "confusion_matrices_per_chunk_03_arimax_steady_12_bp = prepare_df(confusion_matrices_per_chunk_03_arimax_steady_12_bp)\n",
    "\n",
    "confusion_matrices_per_chunk_03_arimax_steady_12_o2 = dict_to_df(accuracy_dict_for_chunk_iterations_arimax_12_o2)\n",
    "confusion_matrices_per_chunk_03_arimax_steady_12_o2 = prepare_df(confusion_matrices_per_chunk_03_arimax_steady_12_o2)\n",
    "\n",
    "# Calculate statistics about the number of chunks as well as chunk iteration combinations\n",
    "\n",
    "stats_03_arimax_steady_12_hr = calc_stats(confusion_matrices_per_chunk_03_arimax_steady_12_hr)\n",
    "\n",
    "stats_03_arimax_steady_12_bp = calc_stats(confusion_matrices_per_chunk_03_arimax_steady_12_bp)\n",
    "\n",
    "stats_03_arimax_steady_12_o2 = calc_stats(confusion_matrices_per_chunk_03_arimax_steady_12_o2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "START transforming dictionary.\n",
      "DONE transforming dictionary.\n",
      "Completed 374 chunks in 0.10011 minutes\n",
      "Number of chunk-iteration combinations: 15766\n",
      "--------------------\n",
      "START transforming dictionary.\n",
      "DONE transforming dictionary.\n",
      "Completed 45 chunks in 0.00573 minutes\n",
      "Number of chunk-iteration combinations: 580\n",
      "--------------------\n",
      "START transforming dictionary.\n",
      "DONE transforming dictionary.\n",
      "Completed 188 chunks in 0.03571 minutes\n",
      "Number of chunk-iteration combinations: 6246\n",
      "--------------------\n"
     ]
    }
   ],
   "source": [
    "# 03 - ARIMAX - Steady Train Size 30\n",
    "# Transform nested dictionary into data frame\n",
    "\n",
    "confusion_matrices_per_chunk_03_arimax_steady_30_hr = dict_to_df(accuracy_dict_for_chunk_iterations_arimax_30_hr)\n",
    "confusion_matrices_per_chunk_03_arimax_steady_30_hr = prepare_df(confusion_matrices_per_chunk_03_arimax_steady_30_hr)\n",
    "\n",
    "confusion_matrices_per_chunk_03_arimax_steady_30_bp = dict_to_df(accuracy_dict_for_chunk_iterations_arimax_30_bp)\n",
    "confusion_matrices_per_chunk_03_arimax_steady_30_bp = prepare_df(confusion_matrices_per_chunk_03_arimax_steady_30_bp)\n",
    "\n",
    "confusion_matrices_per_chunk_03_arimax_steady_30_o2 = dict_to_df(accuracy_dict_for_chunk_iterations_arimax_30_o2)\n",
    "confusion_matrices_per_chunk_03_arimax_steady_30_o2 = prepare_df(confusion_matrices_per_chunk_03_arimax_steady_30_o2)\n",
    "\n",
    "# Calculate statistics about the number of chunks as well as chunk iteration combinations\n",
    "\n",
    "stats_03_arimax_steady_30_hr = calc_stats(confusion_matrices_per_chunk_03_arimax_steady_30_hr)\n",
    "\n",
    "stats_03_arimax_steady_30_bp = calc_stats(confusion_matrices_per_chunk_03_arimax_steady_30_bp)\n",
    "\n",
    "stats_03_arimax_steady_30_o2 = calc_stats(confusion_matrices_per_chunk_03_arimax_steady_30_o2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "START transforming dictionary.\n",
      "DONE transforming dictionary.\n",
      "Completed 671 chunks in 0.15251 minutes\n",
      "Number of chunk-iteration combinations: 25112\n",
      "--------------------\n",
      "START transforming dictionary.\n",
      "DONE transforming dictionary.\n",
      "Completed 207 chunks in 0.02641 minutes\n",
      "Number of chunk-iteration combinations: 2407\n",
      "--------------------\n",
      "START transforming dictionary.\n",
      "DONE transforming dictionary.\n",
      "Completed 438 chunks in 0.07379 minutes\n",
      "Number of chunk-iteration combinations: 11717\n",
      "--------------------\n"
     ]
    }
   ],
   "source": [
    "# 04 - ARIMAX - Expanding Train Size 12\n",
    "# Transform nested dictionary into data frame\n",
    "\n",
    "confusion_matrices_per_chunk_04_arimax_expanding_12_hr = dict_to_df(accuracy_dict_for_chunk_iterations_arimax_expanding_12_hr)\n",
    "confusion_matrices_per_chunk_04_arimax_expanding_12_hr = prepare_df(confusion_matrices_per_chunk_04_arimax_expanding_12_hr)\n",
    "\n",
    "confusion_matrices_per_chunk_04_arimax_expanding_12_bp = dict_to_df(accuracy_dict_for_chunk_iterations_arimax_expanding_12_bp)\n",
    "confusion_matrices_per_chunk_04_arimax_expanding_12_bp = prepare_df(confusion_matrices_per_chunk_04_arimax_expanding_12_bp)\n",
    "\n",
    "confusion_matrices_per_chunk_04_arimax_expanding_12_o2 = dict_to_df(accuracy_dict_for_chunk_iterations_arimax_expanding_12_o2)\n",
    "confusion_matrices_per_chunk_04_arimax_expanding_12_o2 = prepare_df(confusion_matrices_per_chunk_04_arimax_expanding_12_o2)\n",
    "\n",
    "# Calculate statistics about the number of chunks as well as chunk iteration combinations\n",
    "\n",
    "stats_04_arimax_expanding_12_hr = calc_stats(confusion_matrices_per_chunk_04_arimax_expanding_12_hr)\n",
    "\n",
    "stats_04_arimax_expanding_12_bp = calc_stats(confusion_matrices_per_chunk_04_arimax_expanding_12_bp)\n",
    "\n",
    "stats_04_arimax_expanding_12_o2 = calc_stats(confusion_matrices_per_chunk_04_arimax_expanding_12_o2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "START transforming dictionary.\n",
      "DONE transforming dictionary.\n",
      "Completed 374 chunks in 0.10256 minutes\n",
      "Number of chunk-iteration combinations: 15766\n",
      "--------------------\n",
      "START transforming dictionary.\n",
      "DONE transforming dictionary.\n",
      "Completed 45 chunks in 0.00643 minutes\n",
      "Number of chunk-iteration combinations: 580\n",
      "--------------------\n",
      "START transforming dictionary.\n",
      "DONE transforming dictionary.\n",
      "Completed 188 chunks in 0.04497 minutes\n",
      "Number of chunk-iteration combinations: 6246\n",
      "--------------------\n"
     ]
    }
   ],
   "source": [
    "# 04 - ARIMAX - Expanding Train Size 30\n",
    "# Transform nested dictionary into data frame\n",
    "\n",
    "confusion_matrices_per_chunk_04_arimax_expanding_30_hr = dict_to_df(accuracy_dict_for_chunk_iterations_arimax_expanding_30_hr)\n",
    "confusion_matrices_per_chunk_04_arimax_expanding_30_hr = prepare_df(confusion_matrices_per_chunk_04_arimax_expanding_30_hr)\n",
    "\n",
    "confusion_matrices_per_chunk_04_arimax_expanding_30_bp = dict_to_df(accuracy_dict_for_chunk_iterations_arimax_expanding_30_bp)\n",
    "confusion_matrices_per_chunk_04_arimax_expanding_30_bp = prepare_df(confusion_matrices_per_chunk_04_arimax_expanding_30_bp)\n",
    "\n",
    "confusion_matrices_per_chunk_04_arimax_expanding_30_o2 = dict_to_df(accuracy_dict_for_chunk_iterations_arimax_expanding_30_o2)\n",
    "confusion_matrices_per_chunk_04_arimax_expanding_30_o2 = prepare_df(confusion_matrices_per_chunk_04_arimax_expanding_30_o2)\n",
    "\n",
    "# Calculate statistics about the number of chunks as well as chunk iteration combinations\n",
    "\n",
    "stats_04_arimax_expanding_30_hr = calc_stats(confusion_matrices_per_chunk_04_arimax_expanding_30_hr)\n",
    "\n",
    "stats_04_arimax_expanding_30_bp = calc_stats(confusion_matrices_per_chunk_04_arimax_expanding_30_bp)\n",
    "\n",
    "stats_04_arimax_expanding_30_o2 = calc_stats(confusion_matrices_per_chunk_04_arimax_expanding_30_o2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "START transforming dictionary.\n",
      "DONE transforming dictionary.\n",
      "Completed 374 chunks in 0.0943 minutes\n",
      "Number of chunk-iteration combinations: 15766\n",
      "--------------------\n",
      "START transforming dictionary.\n",
      "DONE transforming dictionary.\n",
      "Completed 45 chunks in 0.00625 minutes\n",
      "Number of chunk-iteration combinations: 580\n",
      "--------------------\n",
      "START transforming dictionary.\n",
      "DONE transforming dictionary.\n",
      "Completed 188 chunks in 0.05272 minutes\n",
      "Number of chunk-iteration combinations: 6246\n",
      "--------------------\n"
     ]
    }
   ],
   "source": [
    "# 05 - Darts ARIMA - Steady Train Size 30\n",
    "# Transform nested dictionary into data frame\n",
    "\n",
    "confusion_matrices_per_chunk_05_darts_arima_30_hr = dict_to_df(accuracy_dict_for_chunk_iterations_darts_arima_30_hr)\n",
    "confusion_matrices_per_chunk_05_darts_arima_30_hr = prepare_df(confusion_matrices_per_chunk_05_darts_arima_30_hr)\n",
    "\n",
    "confusion_matrices_per_chunk_05_darts_arima_30_bp = dict_to_df(accuracy_dict_for_chunk_iterations_darts_arima_30_bp)\n",
    "confusion_matrices_per_chunk_05_darts_arima_30_bp = prepare_df(confusion_matrices_per_chunk_05_darts_arima_30_bp)\n",
    "\n",
    "confusion_matrices_per_chunk_05_darts_arima_30_o2 = dict_to_df(accuracy_dict_for_chunk_iterations_darts_arima_30_o2)\n",
    "confusion_matrices_per_chunk_05_darts_arima_30_o2 = prepare_df(confusion_matrices_per_chunk_05_darts_arima_30_o2)\n",
    "\n",
    "# Calculate statistics about the number of chunks as well as chunk iteration combinations\n",
    "\n",
    "stats_05_darts_arima_30_hr = calc_stats(confusion_matrices_per_chunk_05_darts_arima_30_hr)\n",
    "\n",
    "stats_05_darts_arima_30_bp = calc_stats(confusion_matrices_per_chunk_05_darts_arima_30_bp)\n",
    "\n",
    "stats_05_darts_arima_30_o2 = calc_stats(confusion_matrices_per_chunk_05_darts_arima_30_o2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "START transforming dictionary.\n",
      "DONE transforming dictionary.\n",
      "Completed 374 chunks in 0.09378 minutes\n",
      "Number of chunk-iteration combinations: 15766\n",
      "--------------------\n",
      "START transforming dictionary.\n",
      "DONE transforming dictionary.\n",
      "Completed 45 chunks in 0.0062 minutes\n",
      "Number of chunk-iteration combinations: 580\n",
      "--------------------\n",
      "START transforming dictionary.\n",
      "DONE transforming dictionary.\n",
      "Completed 188 chunks in 0.03779 minutes\n",
      "Number of chunk-iteration combinations: 6246\n",
      "--------------------\n"
     ]
    }
   ],
   "source": [
    "# 06 - Darts ARIMA - Expanding Train Size 30\n",
    "# Transform nested dictionary into data frame\n",
    "\n",
    "confusion_matrices_per_chunk_06_darts_arima_expanding_30_hr = dict_to_df(accuracy_dict_for_chunk_iterations_darts_arima_expanding_30_hr)\n",
    "confusion_matrices_per_chunk_06_darts_arima_expanding_30_hr = prepare_df(confusion_matrices_per_chunk_06_darts_arima_expanding_30_hr)\n",
    "\n",
    "confusion_matrices_per_chunk_06_darts_arima_expanding_30_bp = dict_to_df(accuracy_dict_for_chunk_iterations_darts_arima_expanding_30_bp)\n",
    "confusion_matrices_per_chunk_06_darts_arima_expanding_30_bp = prepare_df(confusion_matrices_per_chunk_06_darts_arima_expanding_30_bp)\n",
    "\n",
    "confusion_matrices_per_chunk_06_darts_arima_expanding_30_o2 = dict_to_df(accuracy_dict_for_chunk_iterations_darts_arima_expanding_30_o2)\n",
    "confusion_matrices_per_chunk_06_darts_arima_expanding_30_o2 = prepare_df(confusion_matrices_per_chunk_06_darts_arima_expanding_30_o2)\n",
    "\n",
    "# Calculate statistics about the number of chunks as well as chunk iteration combinations\n",
    "\n",
    "stats_06_darts_arima_expanding_30_hr = calc_stats(confusion_matrices_per_chunk_06_darts_arima_expanding_30_hr)\n",
    "\n",
    "stats_06_darts_arima_expanding_30_bp = calc_stats(confusion_matrices_per_chunk_06_darts_arima_expanding_30_bp)\n",
    "\n",
    "stats_06_darts_arima_expanding_30_o2 = calc_stats(confusion_matrices_per_chunk_06_darts_arima_expanding_30_o2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "START transforming dictionary.\n",
      "DONE transforming dictionary.\n",
      "Completed 374 chunks in 0.09501 minutes\n",
      "Number of chunk-iteration combinations: 15766\n",
      "--------------------\n",
      "START transforming dictionary.\n",
      "DONE transforming dictionary.\n",
      "Completed 45 chunks in 0.00613 minutes\n",
      "Number of chunk-iteration combinations: 580\n",
      "--------------------\n",
      "START transforming dictionary.\n",
      "DONE transforming dictionary.\n",
      "Completed 188 chunks in 0.03659 minutes\n",
      "Number of chunk-iteration combinations: 6246\n",
      "--------------------\n"
     ]
    }
   ],
   "source": [
    "# 07 - Darts ARIMAX - Steady Train Size 30\n",
    "# Transform nested dictionary into data frame\n",
    "\n",
    "confusion_matrices_per_chunk_07_darts_arimax_30_hr = dict_to_df(accuracy_dict_for_chunk_iterations_darts_arimax_30_hr)\n",
    "confusion_matrices_per_chunk_07_darts_arimax_30_hr = prepare_df(confusion_matrices_per_chunk_07_darts_arimax_30_hr)\n",
    "\n",
    "confusion_matrices_per_chunk_07_darts_arimax_30_bp = dict_to_df(accuracy_dict_for_chunk_iterations_darts_arimax_30_bp)\n",
    "confusion_matrices_per_chunk_07_darts_arimax_30_bp = prepare_df(confusion_matrices_per_chunk_07_darts_arimax_30_bp)\n",
    "\n",
    "confusion_matrices_per_chunk_07_darts_arimax_30_o2 = dict_to_df(accuracy_dict_for_chunk_iterations_darts_arimax_30_o2)\n",
    "confusion_matrices_per_chunk_07_darts_arimax_30_o2 = prepare_df(confusion_matrices_per_chunk_07_darts_arimax_30_o2)\n",
    "\n",
    "# Calculate statistics about the number of chunks as well as chunk iteration combinations\n",
    "\n",
    "stats_07_darts_arimax_30_hr = calc_stats(confusion_matrices_per_chunk_07_darts_arimax_30_hr)\n",
    "\n",
    "stats_07_darts_arimax_30_bp = calc_stats(confusion_matrices_per_chunk_07_darts_arimax_30_bp)\n",
    "\n",
    "stats_07_darts_arimax_30_o2 = calc_stats(confusion_matrices_per_chunk_07_darts_arimax_30_o2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "START transforming dictionary.\n",
      "DONE transforming dictionary.\n",
      "Completed 374 chunks in 0.07795 minutes\n",
      "Number of chunk-iteration combinations: 15766\n",
      "--------------------\n",
      "START transforming dictionary.\n",
      "DONE transforming dictionary.\n",
      "Completed 45 chunks in 0.00778 minutes\n",
      "Number of chunk-iteration combinations: 580\n",
      "--------------------\n",
      "START transforming dictionary.\n",
      "DONE transforming dictionary.\n",
      "Completed 188 chunks in 0.02952 minutes\n",
      "Number of chunk-iteration combinations: 6246\n",
      "--------------------\n"
     ]
    }
   ],
   "source": [
    "# 08 - Darts ARIMAX - Expanding Train Size 30\n",
    "# Transform nested dictionary into data frame\n",
    "\n",
    "confusion_matrices_per_chunk_08_darts_arimax_expanding_30_hr = dict_to_df(accuracy_dict_for_chunk_iterations_darts_arimax_expanding_30_hr)\n",
    "confusion_matrices_per_chunk_08_darts_arimax_expanding_30_hr = prepare_df(confusion_matrices_per_chunk_08_darts_arimax_expanding_30_hr)\n",
    "\n",
    "confusion_matrices_per_chunk_08_darts_arimax_expanding_30_bp = dict_to_df(accuracy_dict_for_chunk_iterations_darts_arimax_expanding_30_bp)\n",
    "confusion_matrices_per_chunk_08_darts_arimax_expanding_30_bp = prepare_df(confusion_matrices_per_chunk_08_darts_arimax_expanding_30_bp)\n",
    "\n",
    "confusion_matrices_per_chunk_08_darts_arimax_expanding_30_o2 = dict_to_df(accuracy_dict_for_chunk_iterations_darts_arimax_expanding_30_o2)\n",
    "confusion_matrices_per_chunk_08_darts_arimax_expanding_30_o2 = prepare_df(confusion_matrices_per_chunk_08_darts_arimax_expanding_30_o2)\n",
    "\n",
    "# Calculate statistics about the number of chunks as well as chunk iteration combinations\n",
    "\n",
    "stats_08_darts_arimax_expanding_30_hr = calc_stats(confusion_matrices_per_chunk_08_darts_arimax_expanding_30_hr)\n",
    "\n",
    "stats_08_darts_arimax_expanding_30_bp = calc_stats(confusion_matrices_per_chunk_08_darts_arimax_expanding_30_bp)\n",
    "\n",
    "stats_08_darts_arimax_expanding_30_o2 = calc_stats(confusion_matrices_per_chunk_08_darts_arimax_expanding_30_o2)"
   ]
  },
  {
   "source": [
    "## Analyze Data"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 01 - ARIMA - Steady TRAIN Size 12\n",
    "\n",
    "# Create confusion matrix per chunk by aggregating so that TP, FN, FP, and TN of the several chunk iterations are summed up\n",
    "\n",
    "confusion_matrix_per_chunk_01_arima_steady_12_hr = aggregate_to_confusion_matrix_per_chunk(confusion_matrices_per_chunk_01_arima_steady_12_hr)\n",
    "\n",
    "confusion_matrix_per_chunk_01_arima_steady_12_bp = aggregate_to_confusion_matrix_per_chunk(confusion_matrices_per_chunk_01_arima_steady_12_bp)\n",
    "\n",
    "confusion_matrix_per_chunk_01_arima_steady_12_o2 = aggregate_to_confusion_matrix_per_chunk(confusion_matrices_per_chunk_01_arima_steady_12_o2)\n",
    "\n",
    "# Create confusion matrix by aggregating so that TP, FN, FP, and TN of all chunks are summed up\n",
    "\n",
    "confusion_matrix_01_arima_steady_12_hr = aggregate_to_confusion_matrix_per_model(confusion_matrices_per_chunk_01_arima_steady_12_hr)\n",
    "\n",
    "confusion_matrix_01_arima_steady_12_bp = aggregate_to_confusion_matrix_per_model(confusion_matrices_per_chunk_01_arima_steady_12_bp)\n",
    "\n",
    "confusion_matrix_01_arima_steady_12_o2 = aggregate_to_confusion_matrix_per_model(confusion_matrices_per_chunk_01_arima_steady_12_o2)\n",
    "\n",
    "# Calculate statistical measures for evaluation of the model\n",
    "\n",
    "metrics_01_arima_steady_12_hr = calc_metrics(confusion_matrix_01_arima_steady_12_hr)\n",
    "\n",
    "metrics_01_arima_steady_12_bp = calc_metrics(confusion_matrix_01_arima_steady_12_bp)\n",
    "\n",
    "metrics_01_arima_steady_12_o2 = calc_metrics(confusion_matrix_01_arima_steady_12_o2)\n",
    "\n",
    "# Create overview table\n",
    "\n",
    "model_01_arima_steady_12 = pd.DataFrame({\n",
    "    'MODEL':            ['ARIMA',       'ARIMA'],\n",
    "    'RESAMPLING':       ['Median',      'Median'],\n",
    "    'EXOGENOUS':        ['n/a',         'n/a'],\n",
    "    'TRAINSIZE':        ['Steady',      'Steady'],\n",
    "    'FIRSTFORECAST':    ['12',          '12']\n",
    "    })\n",
    "\n",
    "overview_01_arima_steady_12_hr = pd.concat([\n",
    "    pd.DataFrame({'ID': ['HR_A_01_12_H', 'HR_A_01_12_L']}),\n",
    "    pd.DataFrame({'PARAMETER': ['HR']}),\n",
    "    model_01_arima_steady_12,\n",
    "    confusion_matrix_01_arima_steady_12_hr.reset_index(),\n",
    "    metrics_01_arima_steady_12_bp.loc[:, metrics_01_arima_steady_12_bp.columns != 'ALARMTYPE'],\n",
    "    stats_01_arima_steady_12_hr], axis=1).pad()\n",
    "\n",
    "overview_01_arima_steady_12_bp = pd.concat([\n",
    "    pd.DataFrame({'ID': ['BP_A_01_12_H', 'BP_A_01_12_L']}),\n",
    "    pd.DataFrame({'PARAMETER': ['BP']}),\n",
    "    model_01_arima_steady_12,\n",
    "    confusion_matrix_01_arima_steady_12_bp.reset_index(),\n",
    "    metrics_01_arima_steady_12_bp.loc[:, metrics_01_arima_steady_12_bp.columns != 'ALARMTYPE'],\n",
    "    stats_01_arima_steady_12_bp], axis=1).pad()\n",
    "\n",
    "overview_01_arima_steady_12_o2 = pd.concat([\n",
    "    pd.DataFrame({'ID': ['O2_A_01_12_H', 'O2_A_01_12_L']}),\n",
    "    pd.DataFrame({'PARAMETER': ['O2']}),\n",
    "    model_01_arima_steady_12,\n",
    "    confusion_matrix_01_arima_steady_12_o2.reset_index(),\n",
    "    metrics_01_arima_steady_12_o2.loc[:, metrics_01_arima_steady_12_o2.columns != 'ALARMTYPE'],\n",
    "    stats_01_arima_steady_12_o2], axis=1).pad()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "<ipython-input-11-0c679fe70579>:83: RuntimeWarning: invalid value encountered in longlong_scalars\n  tpr_high = TP_high/(TP_high+FN_high)\n<ipython-input-11-0c679fe70579>:84: RuntimeWarning: invalid value encountered in longlong_scalars\n  fnr_high = FN_high/(TP_high+FN_high)\n"
     ]
    }
   ],
   "source": [
    "# 01 - ARIMA - Steady TRAIN Size 30\n",
    "\n",
    "# Create confusion matrix per chunk by aggregating so that TP, FN, FP, and TN of the several chunk iterations are summed up\n",
    "\n",
    "confusion_matrix_per_chunk_01_arima_steady_30_hr = aggregate_to_confusion_matrix_per_chunk(confusion_matrices_per_chunk_01_arima_steady_30_hr)\n",
    "\n",
    "confusion_matrix_per_chunk_01_arima_steady_30_bp = aggregate_to_confusion_matrix_per_chunk(confusion_matrices_per_chunk_01_arima_steady_30_bp)\n",
    "\n",
    "confusion_matrix_per_chunk_01_arima_steady_30_o2 = aggregate_to_confusion_matrix_per_chunk(confusion_matrices_per_chunk_01_arima_steady_30_o2)\n",
    "\n",
    "# Create confusion matrix by aggregating so that TP, FN, FP, and TN of all chunks are summed up\n",
    "\n",
    "confusion_matrix_01_arima_steady_30_hr = aggregate_to_confusion_matrix_per_model(confusion_matrices_per_chunk_01_arima_steady_30_hr)\n",
    "\n",
    "confusion_matrix_01_arima_steady_30_bp = aggregate_to_confusion_matrix_per_model(confusion_matrices_per_chunk_01_arima_steady_30_bp)\n",
    "\n",
    "confusion_matrix_01_arima_steady_30_o2 = aggregate_to_confusion_matrix_per_model(confusion_matrices_per_chunk_01_arima_steady_30_o2)\n",
    "\n",
    "# Calculate statistical measures for evaluation of the model\n",
    "\n",
    "metrics_01_arima_steady_30_hr = calc_metrics(confusion_matrix_01_arima_steady_30_hr)\n",
    "\n",
    "metrics_01_arima_steady_30_bp = calc_metrics(confusion_matrix_01_arima_steady_30_bp)\n",
    "\n",
    "metrics_01_arima_steady_30_o2 = calc_metrics(confusion_matrix_01_arima_steady_30_o2)\n",
    "\n",
    "# Create overview table\n",
    "\n",
    "model_01_arima_steady_30 = pd.DataFrame({\n",
    "    'MODEL':            ['ARIMA',       'ARIMA'],\n",
    "    'RESAMPLING':       ['Median',      'Median'],\n",
    "    'EXOGENOUS':        ['n/a',         'n/a'],\n",
    "    'TRAINSIZE':        ['Steady',      'Steady'],\n",
    "    'FIRSTFORECAST':    ['30',          '30']\n",
    "    })\n",
    "\n",
    "overview_01_arima_steady_30_hr = pd.concat([\n",
    "    pd.DataFrame({'ID': ['HR_A_01_30_H', 'HR_A_01_30_L']}),\n",
    "    pd.DataFrame({'PARAMETER': ['HR']}),\n",
    "    model_01_arima_steady_30,\n",
    "    confusion_matrix_01_arima_steady_30_hr.reset_index(),\n",
    "    metrics_01_arima_steady_30_bp.loc[:, metrics_01_arima_steady_30_bp.columns != 'ALARMTYPE'],\n",
    "    stats_01_arima_steady_30_hr], axis=1).pad()\n",
    "\n",
    "overview_01_arima_steady_30_bp = pd.concat([\n",
    "    pd.DataFrame({'ID': ['BP_A_01_30_H', 'BP_A_01_30_L']}),\n",
    "    pd.DataFrame({'PARAMETER': ['BP']}),\n",
    "    model_01_arima_steady_30,\n",
    "    confusion_matrix_01_arima_steady_30_bp.reset_index(),\n",
    "    metrics_01_arima_steady_30_bp.loc[:, metrics_01_arima_steady_30_bp.columns != 'ALARMTYPE'],\n",
    "    stats_01_arima_steady_30_bp], axis=1).pad()\n",
    "\n",
    "overview_01_arima_steady_30_o2 = pd.concat([\n",
    "    pd.DataFrame({'ID': ['O2_A_01_30_H', 'O2_A_01_30_L']}),\n",
    "    pd.DataFrame({'PARAMETER': ['O2']}),\n",
    "    model_01_arima_steady_30,\n",
    "    confusion_matrix_01_arima_steady_30_o2.reset_index(),\n",
    "    metrics_01_arima_steady_30_o2.loc[:, metrics_01_arima_steady_30_o2.columns != 'ALARMTYPE'],\n",
    "    stats_01_arima_steady_30_o2], axis=1).pad()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 02 - ARIMA - Expanding TRAIN Size 12\n",
    "\n",
    "# Create confusion matrix per chunk by aggregating so that TP, FN, FP, and TN of the several chunk iterations are summed up\n",
    "\n",
    "confusion_matrix_per_chunk_02_arima_expanding_12_hr = aggregate_to_confusion_matrix_per_chunk(confusion_matrices_per_chunk_02_arima_expanding_12_hr)\n",
    "\n",
    "confusion_matrix_per_chunk_02_arima_expanding_12_bp = aggregate_to_confusion_matrix_per_chunk(confusion_matrices_per_chunk_02_arima_expanding_12_bp)\n",
    "\n",
    "confusion_matrix_per_chunk_02_arima_expanding_12_o2 = aggregate_to_confusion_matrix_per_chunk(confusion_matrices_per_chunk_02_arima_expanding_12_o2)\n",
    "\n",
    "# Create confusion matrix by aggregating so that TP, FN, FP, and TN of all chunks are summed up\n",
    "\n",
    "confusion_matrix_02_arima_expanding_12_hr = aggregate_to_confusion_matrix_per_model(confusion_matrices_per_chunk_02_arima_expanding_12_hr)\n",
    "\n",
    "confusion_matrix_02_arima_expanding_12_bp = aggregate_to_confusion_matrix_per_model(confusion_matrices_per_chunk_02_arima_expanding_12_bp)\n",
    "\n",
    "confusion_matrix_02_arima_expanding_12_o2 = aggregate_to_confusion_matrix_per_model(confusion_matrices_per_chunk_02_arima_expanding_12_o2)\n",
    "\n",
    "# Calculate statistical measures for evaluation of the model\n",
    "\n",
    "metrics_02_arima_expanding_12_hr = calc_metrics(confusion_matrix_02_arima_expanding_12_hr)\n",
    "\n",
    "metrics_02_arima_expanding_12_bp = calc_metrics(confusion_matrix_02_arima_expanding_12_bp)\n",
    "\n",
    "metrics_02_arima_expanding_12_o2 = calc_metrics(confusion_matrix_02_arima_expanding_12_o2)\n",
    "\n",
    "# Create overview table\n",
    "\n",
    "model_02_arima_expanding_12 = pd.DataFrame({\n",
    "    'MODEL':            ['ARIMA',           'ARIMA'],\n",
    "    'RESAMPLING':       ['Median',          'Median'],\n",
    "    'EXOGENOUS':        ['n/a',             'n/a'],\n",
    "    'TRAINSIZE':        ['Expanding',       'Expanding'],\n",
    "    'FIRSTFORECAST':    ['12',              '12']\n",
    "    })\n",
    "\n",
    "overview_02_arima_expanding_12_hr = pd.concat([\n",
    "    pd.DataFrame({'ID': ['HR_A_02_12_H', 'HR_A_02_12_L']}),\n",
    "    pd.DataFrame({'PARAMETER': ['HR']}),\n",
    "    model_02_arima_expanding_12,\n",
    "    confusion_matrix_02_arima_expanding_12_hr.reset_index(),\n",
    "    metrics_02_arima_expanding_12_bp.loc[:, metrics_02_arima_expanding_12_bp.columns != 'ALARMTYPE'],\n",
    "    stats_02_arima_expanding_12_hr], axis=1).pad()\n",
    "\n",
    "overview_02_arima_expanding_12_bp = pd.concat([\n",
    "    pd.DataFrame({'ID': ['BP_A_02_12_H', 'BP_A_02_12_L']}),\n",
    "    pd.DataFrame({'PARAMETER': ['BP']}),\n",
    "    model_02_arima_expanding_12,\n",
    "    confusion_matrix_02_arima_expanding_12_bp.reset_index(),\n",
    "    metrics_02_arima_expanding_12_bp.loc[:, metrics_02_arima_expanding_12_bp.columns != 'ALARMTYPE'],\n",
    "    stats_02_arima_expanding_12_bp], axis=1).pad()\n",
    "\n",
    "overview_02_arima_expanding_12_o2 = pd.concat([\n",
    "    pd.DataFrame({'ID': ['O2_A_02_12_H', 'O2_A_02_12_L']}),\n",
    "    pd.DataFrame({'PARAMETER': ['O2']}),\n",
    "    model_02_arima_expanding_12,\n",
    "    confusion_matrix_02_arima_expanding_12_o2.reset_index(),\n",
    "    metrics_02_arima_expanding_12_o2.loc[:, metrics_02_arima_expanding_12_o2.columns != 'ALARMTYPE'],\n",
    "    stats_02_arima_expanding_12_o2], axis=1).pad()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "<ipython-input-11-0c679fe70579>:83: RuntimeWarning: invalid value encountered in longlong_scalars\n  tpr_high = TP_high/(TP_high+FN_high)\n<ipython-input-11-0c679fe70579>:84: RuntimeWarning: invalid value encountered in longlong_scalars\n  fnr_high = FN_high/(TP_high+FN_high)\n"
     ]
    }
   ],
   "source": [
    "# 02 - ARIMA - Expanding TRAIN Size 30\n",
    "\n",
    "# Create confusion matrix per chunk by aggregating so that TP, FN, FP, and TN of the several chunk iterations are summed up\n",
    "\n",
    "confusion_matrix_per_chunk_02_arima_expanding_30_hr = aggregate_to_confusion_matrix_per_chunk(confusion_matrices_per_chunk_02_arima_expanding_30_hr)\n",
    "\n",
    "confusion_matrix_per_chunk_02_arima_expanding_30_bp = aggregate_to_confusion_matrix_per_chunk(confusion_matrices_per_chunk_02_arima_expanding_30_bp)\n",
    "\n",
    "confusion_matrix_per_chunk_02_arima_expanding_30_o2 = aggregate_to_confusion_matrix_per_chunk(confusion_matrices_per_chunk_02_arima_expanding_30_o2)\n",
    "\n",
    "# Create confusion matrix by aggregating so that TP, FN, FP, and TN of all chunks are summed up\n",
    "\n",
    "confusion_matrix_02_arima_expanding_30_hr = aggregate_to_confusion_matrix_per_model(confusion_matrices_per_chunk_02_arima_expanding_30_hr)\n",
    "\n",
    "confusion_matrix_02_arima_expanding_30_bp = aggregate_to_confusion_matrix_per_model(confusion_matrices_per_chunk_02_arima_expanding_30_bp)\n",
    "\n",
    "confusion_matrix_02_arima_expanding_30_o2 = aggregate_to_confusion_matrix_per_model(confusion_matrices_per_chunk_02_arima_expanding_30_o2)\n",
    "\n",
    "# Calculate statistical measures for evaluation of the model\n",
    "\n",
    "metrics_02_arima_expanding_30_hr = calc_metrics(confusion_matrix_02_arima_expanding_30_hr)\n",
    "\n",
    "metrics_02_arima_expanding_30_bp = calc_metrics(confusion_matrix_02_arima_expanding_30_bp)\n",
    "\n",
    "metrics_02_arima_expanding_30_o2 = calc_metrics(confusion_matrix_02_arima_expanding_30_o2)\n",
    "\n",
    "# Create overview table\n",
    "\n",
    "model_02_arima_expanding_30 = pd.DataFrame({\n",
    "    'MODEL':            ['ARIMA',           'ARIMA'],\n",
    "    'RESAMPLING':       ['Median',          'Median'],\n",
    "    'EXOGENOUS':        ['n/a',             'n/a'],\n",
    "    'TRAINSIZE':        ['Expanding',       'Expanding'],\n",
    "    'FIRSTFORECAST':    ['30',              '30']\n",
    "    })\n",
    "\n",
    "overview_02_arima_expanding_30_hr = pd.concat([\n",
    "    pd.DataFrame({'ID': ['HR_A_02_30_H', 'HR_A_02_30_L']}),\n",
    "    pd.DataFrame({'PARAMETER': ['HR']}),\n",
    "    model_02_arima_expanding_30,\n",
    "    confusion_matrix_02_arima_expanding_30_hr.reset_index(),\n",
    "    metrics_02_arima_expanding_30_bp.loc[:, metrics_02_arima_expanding_30_bp.columns != 'ALARMTYPE'],\n",
    "    stats_02_arima_expanding_30_hr], axis=1).pad()\n",
    "\n",
    "overview_02_arima_expanding_30_bp = pd.concat([\n",
    "    pd.DataFrame({'ID': ['BP_A_02_30_H', 'BP_A_02_30_L']}),\n",
    "    pd.DataFrame({'PARAMETER': ['BP']}),\n",
    "    model_02_arima_expanding_30,\n",
    "    confusion_matrix_02_arima_expanding_30_bp.reset_index(),\n",
    "    metrics_02_arima_expanding_30_bp.loc[:, metrics_02_arima_expanding_30_bp.columns != 'ALARMTYPE'],\n",
    "    stats_02_arima_expanding_30_bp], axis=1).pad()\n",
    "\n",
    "overview_02_arima_expanding_30_o2 = pd.concat([\n",
    "    pd.DataFrame({'ID': ['O2_A_02_30_H', 'O2_A_02_30_L']}),\n",
    "    pd.DataFrame({'PARAMETER': ['O2']}),\n",
    "    model_02_arima_expanding_30,\n",
    "    confusion_matrix_02_arima_expanding_30_o2.reset_index(),\n",
    "    metrics_02_arima_expanding_30_o2.loc[:, metrics_02_arima_expanding_30_o2.columns != 'ALARMTYPE'],\n",
    "    stats_02_arima_expanding_30_o2], axis=1).pad()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 03 - ARIMAX - Steady TRAIN Size 12\n",
    "\n",
    "# Create confusion matrix per chunk by aggregating so that TP, FN, FP, and TN of the several chunk iterations are summed up\n",
    "\n",
    "confusion_matrix_per_chunk_03_arimax_steady_12_hr = aggregate_to_confusion_matrix_per_chunk(confusion_matrices_per_chunk_03_arimax_steady_12_hr)\n",
    "\n",
    "confusion_matrix_per_chunk_03_arimax_steady_12_bp = aggregate_to_confusion_matrix_per_chunk(confusion_matrices_per_chunk_03_arimax_steady_12_bp)\n",
    "\n",
    "confusion_matrix_per_chunk_03_arimax_steady_12_o2 = aggregate_to_confusion_matrix_per_chunk(confusion_matrices_per_chunk_03_arimax_steady_12_o2)\n",
    "\n",
    "# Create confusion matrix by aggregating so that TP, FN, FP, and TN of all chunks are summed up\n",
    "\n",
    "confusion_matrix_03_arimax_steady_12_hr = aggregate_to_confusion_matrix_per_model(confusion_matrices_per_chunk_03_arimax_steady_12_hr)\n",
    "\n",
    "confusion_matrix_03_arimax_steady_12_bp = aggregate_to_confusion_matrix_per_model(confusion_matrices_per_chunk_03_arimax_steady_12_bp)\n",
    "\n",
    "confusion_matrix_03_arimax_steady_12_o2 = aggregate_to_confusion_matrix_per_model(confusion_matrices_per_chunk_03_arimax_steady_12_o2)\n",
    "\n",
    "# Calculate statistical measures for evaluation of the model\n",
    "\n",
    "metrics_03_arimax_steady_12_hr = calc_metrics(confusion_matrix_03_arimax_steady_12_hr)\n",
    "\n",
    "metrics_03_arimax_steady_12_bp = calc_metrics(confusion_matrix_03_arimax_steady_12_bp)\n",
    "\n",
    "metrics_03_arimax_steady_12_o2 = calc_metrics(confusion_matrix_03_arimax_steady_12_o2)\n",
    "\n",
    "# Create overview table\n",
    "\n",
    "model_03_arimax_steady_12 = pd.DataFrame({\n",
    "    'MODEL':            ['ARIMAX',      'ARIMAX'],\n",
    "    'RESAMPLING':       ['Max',         'Min'],\n",
    "    'EXOGENOUS':        ['Median',      'Median'],\n",
    "    'TRAINSIZE':        ['Steady',      'Steady'],\n",
    "    'FIRSTFORECAST':    ['12',          '12']\n",
    "    })\n",
    "\n",
    "overview_03_arimax_steady_12_hr = pd.concat([\n",
    "    pd.DataFrame({'ID': ['HR_A_03_12_H', 'HR_A_03_12_L']}),\n",
    "    pd.DataFrame({'PARAMETER': ['HR']}),\n",
    "    model_03_arimax_steady_12,\n",
    "    confusion_matrix_03_arimax_steady_12_hr.reset_index(),\n",
    "    metrics_03_arimax_steady_12_bp.loc[:, metrics_03_arimax_steady_12_bp.columns != 'ALARMTYPE'],\n",
    "    stats_03_arimax_steady_12_hr], axis=1).pad()\n",
    "\n",
    "overview_03_arimax_steady_12_bp = pd.concat([\n",
    "    pd.DataFrame({'ID': ['BP_A_03_12_H', 'BP_A_03_12_L']}),\n",
    "    pd.DataFrame({'PARAMETER': ['BP']}),\n",
    "    model_03_arimax_steady_12,\n",
    "    confusion_matrix_03_arimax_steady_12_bp.reset_index(),\n",
    "    metrics_03_arimax_steady_12_bp.loc[:, metrics_03_arimax_steady_12_bp.columns != 'ALARMTYPE'],\n",
    "    stats_03_arimax_steady_12_bp], axis=1).pad()\n",
    "\n",
    "overview_03_arimax_steady_12_o2 = pd.concat([\n",
    "    pd.DataFrame({'ID': ['O2_A_03_12_H', 'O2_A_03_12_L']}),\n",
    "    pd.DataFrame({'PARAMETER': ['O2']}),\n",
    "    model_03_arimax_steady_12,\n",
    "    confusion_matrix_03_arimax_steady_12_o2.reset_index(),\n",
    "    metrics_03_arimax_steady_12_o2.loc[:, metrics_03_arimax_steady_12_o2.columns != 'ALARMTYPE'],\n",
    "    stats_03_arimax_steady_12_o2], axis=1).pad()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "<ipython-input-11-0c679fe70579>:83: RuntimeWarning: invalid value encountered in longlong_scalars\n  tpr_high = TP_high/(TP_high+FN_high)\n<ipython-input-11-0c679fe70579>:84: RuntimeWarning: invalid value encountered in longlong_scalars\n  fnr_high = FN_high/(TP_high+FN_high)\n"
     ]
    }
   ],
   "source": [
    "# 03 - ARIMAX - Steady TRAIN Size 30\n",
    "\n",
    "# Create confusion matrix per chunk by aggregating so that TP, FN, FP, and TN of the several chunk iterations are summed up\n",
    "\n",
    "confusion_matrix_per_chunk_03_arimax_steady_30_hr = aggregate_to_confusion_matrix_per_chunk(confusion_matrices_per_chunk_03_arimax_steady_30_hr)\n",
    "\n",
    "confusion_matrix_per_chunk_03_arimax_steady_30_bp = aggregate_to_confusion_matrix_per_chunk(confusion_matrices_per_chunk_03_arimax_steady_30_bp)\n",
    "\n",
    "confusion_matrix_per_chunk_03_arimax_steady_30_o2 = aggregate_to_confusion_matrix_per_chunk(confusion_matrices_per_chunk_03_arimax_steady_30_o2)\n",
    "\n",
    "# Create confusion matrix by aggregating so that TP, FN, FP, and TN of all chunks are summed up\n",
    "\n",
    "confusion_matrix_03_arimax_steady_30_hr = aggregate_to_confusion_matrix_per_model(confusion_matrices_per_chunk_03_arimax_steady_30_hr)\n",
    "\n",
    "confusion_matrix_03_arimax_steady_30_bp = aggregate_to_confusion_matrix_per_model(confusion_matrices_per_chunk_03_arimax_steady_30_bp)\n",
    "\n",
    "confusion_matrix_03_arimax_steady_30_o2 = aggregate_to_confusion_matrix_per_model(confusion_matrices_per_chunk_03_arimax_steady_30_o2)\n",
    "\n",
    "# Calculate statistical measures for evaluation of the model\n",
    "\n",
    "metrics_03_arimax_steady_30_hr = calc_metrics(confusion_matrix_03_arimax_steady_30_hr)\n",
    "\n",
    "metrics_03_arimax_steady_30_bp = calc_metrics(confusion_matrix_03_arimax_steady_30_bp)\n",
    "\n",
    "metrics_03_arimax_steady_30_o2 = calc_metrics(confusion_matrix_03_arimax_steady_30_o2)\n",
    "\n",
    "# Create overview table\n",
    "\n",
    "model_03_arimax_steady_30 = pd.DataFrame({\n",
    "    'MODEL':            ['ARIMAX',      'ARIMAX'],\n",
    "    'RESAMPLING':       ['Max',         'Min'],\n",
    "    'EXOGENOUS':        ['Median',      'Median'],\n",
    "    'TRAINSIZE':        ['Steady',      'Steady'],\n",
    "    'FIRSTFORECAST':    ['30',          '30']\n",
    "    })\n",
    "\n",
    "overview_03_arimax_steady_30_hr = pd.concat([\n",
    "    pd.DataFrame({'ID': ['HR_A_03_30_H', 'HR_A_03_30_L']}),\n",
    "    pd.DataFrame({'PARAMETER': ['HR']}),\n",
    "    model_03_arimax_steady_30,\n",
    "    confusion_matrix_03_arimax_steady_30_hr.reset_index(),\n",
    "    metrics_03_arimax_steady_30_bp.loc[:, metrics_03_arimax_steady_30_bp.columns != 'ALARMTYPE'],\n",
    "    stats_03_arimax_steady_30_hr], axis=1).pad()\n",
    "\n",
    "overview_03_arimax_steady_30_bp = pd.concat([\n",
    "    pd.DataFrame({'ID': ['BP_A_03_30_H', 'BP_A_03_30_L']}),\n",
    "    pd.DataFrame({'PARAMETER': ['BP']}),\n",
    "    model_03_arimax_steady_30,\n",
    "    confusion_matrix_03_arimax_steady_30_bp.reset_index(),\n",
    "    metrics_03_arimax_steady_30_bp.loc[:, metrics_03_arimax_steady_30_bp.columns != 'ALARMTYPE'],\n",
    "    stats_03_arimax_steady_30_bp], axis=1).pad()\n",
    "\n",
    "overview_03_arimax_steady_30_o2 = pd.concat([\n",
    "    pd.DataFrame({'ID': ['O2_A_03_30_H', 'O2_A_03_30_L']}),\n",
    "    pd.DataFrame({'PARAMETER': ['O2']}),\n",
    "    model_03_arimax_steady_30,\n",
    "    confusion_matrix_03_arimax_steady_30_o2.reset_index(),\n",
    "    metrics_03_arimax_steady_30_o2.loc[:, metrics_03_arimax_steady_30_o2.columns != 'ALARMTYPE'],\n",
    "    stats_03_arimax_steady_30_o2], axis=1).pad()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 04 - ARIMAX - Expanding TRAIN Size 12\n",
    "\n",
    "# Create confusion matrix per chunk by aggregating so that TP, FN, FP, and TN of the several chunk iterations are summed up\n",
    "\n",
    "confusion_matrix_per_chunk_04_arimax_expanding_12_hr = aggregate_to_confusion_matrix_per_chunk(confusion_matrices_per_chunk_04_arimax_expanding_12_hr)\n",
    "\n",
    "confusion_matrix_per_chunk_04_arimax_expanding_12_bp = aggregate_to_confusion_matrix_per_chunk(confusion_matrices_per_chunk_04_arimax_expanding_12_bp)\n",
    "\n",
    "confusion_matrix_per_chunk_04_arimax_expanding_12_o2 = aggregate_to_confusion_matrix_per_chunk(confusion_matrices_per_chunk_04_arimax_expanding_12_o2)\n",
    "\n",
    "# Create confusion matrix by aggregating so that TP, FN, FP, and TN of all chunks are summed up\n",
    "\n",
    "confusion_matrix_04_arimax_expanding_12_hr = aggregate_to_confusion_matrix_per_model(confusion_matrices_per_chunk_04_arimax_expanding_12_hr)\n",
    "\n",
    "confusion_matrix_04_arimax_expanding_12_bp = aggregate_to_confusion_matrix_per_model(confusion_matrices_per_chunk_04_arimax_expanding_12_bp)\n",
    "\n",
    "confusion_matrix_04_arimax_expanding_12_o2 = aggregate_to_confusion_matrix_per_model(confusion_matrices_per_chunk_04_arimax_expanding_12_o2)\n",
    "\n",
    "# Calculate statistical measures for evaluation of the model\n",
    "\n",
    "metrics_04_arimax_expanding_12_hr = calc_metrics(confusion_matrix_04_arimax_expanding_12_hr)\n",
    "\n",
    "metrics_04_arimax_expanding_12_bp = calc_metrics(confusion_matrix_04_arimax_expanding_12_bp)\n",
    "\n",
    "metrics_04_arimax_expanding_12_o2 = calc_metrics(confusion_matrix_04_arimax_expanding_12_o2)\n",
    "\n",
    "# Create overview table\n",
    "\n",
    "model_04_arimax_expanding_12 = pd.DataFrame({\n",
    "    'MODEL':            ['ARIMAX',      'ARIMAX'],\n",
    "    'RESAMPLING':       ['Max',         'Min'],\n",
    "    'EXOGENOUS':        ['Median',      'Median'],\n",
    "    'TRAINSIZE':        ['Expanding',   'Expanding'],\n",
    "    'FIRSTFORECAST':    ['12',          '12']\n",
    "    })\n",
    "\n",
    "overview_04_arimax_expanding_12_hr = pd.concat([\n",
    "    pd.DataFrame({'ID': ['HR_A_04_12_H', 'HR_A_04_12_L']}),\n",
    "    pd.DataFrame({'PARAMETER': ['HR']}),\n",
    "    model_04_arimax_expanding_12,\n",
    "    confusion_matrix_04_arimax_expanding_12_hr.reset_index(),\n",
    "    metrics_04_arimax_expanding_12_bp.loc[:, metrics_04_arimax_expanding_12_bp.columns != 'ALARMTYPE'],\n",
    "    stats_04_arimax_expanding_12_hr], axis=1).pad()\n",
    "\n",
    "overview_04_arimax_expanding_12_bp = pd.concat([\n",
    "    pd.DataFrame({'ID': ['BP_A_04_12_H', 'BP_A_04_12_L']}),\n",
    "    pd.DataFrame({'PARAMETER': ['BP']}),\n",
    "    model_04_arimax_expanding_12,\n",
    "    confusion_matrix_04_arimax_expanding_12_bp.reset_index(),\n",
    "    metrics_04_arimax_expanding_12_bp.loc[:, metrics_04_arimax_expanding_12_bp.columns != 'ALARMTYPE'],\n",
    "    stats_04_arimax_expanding_12_bp], axis=1).pad()\n",
    "\n",
    "overview_04_arimax_expanding_12_o2 = pd.concat([\n",
    "    pd.DataFrame({'ID': ['O2_A_04_12_H', 'O2_A_04_12_L']}),\n",
    "    pd.DataFrame({'PARAMETER': ['O2']}),\n",
    "    model_04_arimax_expanding_12,\n",
    "    confusion_matrix_04_arimax_expanding_12_o2.reset_index(),\n",
    "    metrics_04_arimax_expanding_12_o2.loc[:, metrics_04_arimax_expanding_12_o2.columns != 'ALARMTYPE'],\n",
    "    stats_04_arimax_expanding_12_o2], axis=1).pad()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "<ipython-input-11-0c679fe70579>:83: RuntimeWarning: invalid value encountered in longlong_scalars\n  tpr_high = TP_high/(TP_high+FN_high)\n<ipython-input-11-0c679fe70579>:84: RuntimeWarning: invalid value encountered in longlong_scalars\n  fnr_high = FN_high/(TP_high+FN_high)\n"
     ]
    }
   ],
   "source": [
    "# 04 - ARIMAX - Expanding TRAIN Size 30\n",
    "\n",
    "# Create confusion matrix per chunk by aggregating so that TP, FN, FP, and TN of the several chunk iterations are summed up\n",
    "\n",
    "confusion_matrix_per_chunk_04_arimax_expanding_30_hr = aggregate_to_confusion_matrix_per_chunk(confusion_matrices_per_chunk_04_arimax_expanding_30_hr)\n",
    "\n",
    "confusion_matrix_per_chunk_04_arimax_expanding_30_bp = aggregate_to_confusion_matrix_per_chunk(confusion_matrices_per_chunk_04_arimax_expanding_30_bp)\n",
    "\n",
    "confusion_matrix_per_chunk_04_arimax_expanding_30_o2 = aggregate_to_confusion_matrix_per_chunk(confusion_matrices_per_chunk_04_arimax_expanding_30_o2)\n",
    "\n",
    "# Create confusion matrix by aggregating so that TP, FN, FP, and TN of all chunks are summed up\n",
    "\n",
    "confusion_matrix_04_arimax_expanding_30_hr = aggregate_to_confusion_matrix_per_model(confusion_matrices_per_chunk_04_arimax_expanding_30_hr)\n",
    "\n",
    "confusion_matrix_04_arimax_expanding_30_bp = aggregate_to_confusion_matrix_per_model(confusion_matrices_per_chunk_04_arimax_expanding_30_bp)\n",
    "\n",
    "confusion_matrix_04_arimax_expanding_30_o2 = aggregate_to_confusion_matrix_per_model(confusion_matrices_per_chunk_04_arimax_expanding_30_o2)\n",
    "\n",
    "# Calculate statistical measures for evaluation of the model\n",
    "\n",
    "metrics_04_arimax_expanding_30_hr = calc_metrics(confusion_matrix_04_arimax_expanding_30_hr)\n",
    "\n",
    "metrics_04_arimax_expanding_30_bp = calc_metrics(confusion_matrix_04_arimax_expanding_30_bp)\n",
    "\n",
    "metrics_04_arimax_expanding_30_o2 = calc_metrics(confusion_matrix_04_arimax_expanding_30_o2)\n",
    "\n",
    "# Create overview table\n",
    "\n",
    "model_04_arimax_expanding_30 = pd.DataFrame({\n",
    "    'MODEL':            ['ARIMAX',      'ARIMAX'],\n",
    "    'RESAMPLING':       ['Max',         'Min'],\n",
    "    'EXOGENOUS':        ['Median',      'Median'],\n",
    "    'TRAINSIZE':        ['Expanding',   'Expanding'],\n",
    "    'FIRSTFORECAST':    ['30',          '30']\n",
    "    })\n",
    "\n",
    "overview_04_arimax_expanding_30_hr = pd.concat([\n",
    "    pd.DataFrame({'ID': ['HR_A_04_30_H', 'HR_A_04_30_L']}),\n",
    "    pd.DataFrame({'PARAMETER': ['HR']}),\n",
    "    model_04_arimax_expanding_30,\n",
    "    confusion_matrix_04_arimax_expanding_30_hr.reset_index(),\n",
    "    metrics_04_arimax_expanding_30_bp.loc[:, metrics_04_arimax_expanding_30_bp.columns != 'ALARMTYPE'],\n",
    "    stats_04_arimax_expanding_30_hr], axis=1).pad()\n",
    "\n",
    "overview_04_arimax_expanding_30_bp = pd.concat([\n",
    "    pd.DataFrame({'ID': ['BP_A_04_30_H', 'BP_A_04_30_L']}),\n",
    "    pd.DataFrame({'PARAMETER': ['BP']}),\n",
    "    model_04_arimax_expanding_30,\n",
    "    confusion_matrix_04_arimax_expanding_30_bp.reset_index(),\n",
    "    metrics_04_arimax_expanding_30_bp.loc[:, metrics_04_arimax_expanding_30_bp.columns != 'ALARMTYPE'],\n",
    "    stats_04_arimax_expanding_30_bp], axis=1).pad()\n",
    "\n",
    "overview_04_arimax_expanding_30_o2 = pd.concat([\n",
    "    pd.DataFrame({'ID': ['O2_A_04_30_H', 'O2_A_04_30_L']}),\n",
    "    pd.DataFrame({'PARAMETER': ['O2']}),\n",
    "    model_04_arimax_expanding_30,\n",
    "    confusion_matrix_04_arimax_expanding_30_o2.reset_index(),\n",
    "    metrics_04_arimax_expanding_30_o2.loc[:, metrics_04_arimax_expanding_30_o2.columns != 'ALARMTYPE'],\n",
    "    stats_04_arimax_expanding_30_o2], axis=1).pad()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "<ipython-input-11-0c679fe70579>:83: RuntimeWarning: invalid value encountered in longlong_scalars\n  tpr_high = TP_high/(TP_high+FN_high)\n<ipython-input-11-0c679fe70579>:84: RuntimeWarning: invalid value encountered in longlong_scalars\n  fnr_high = FN_high/(TP_high+FN_high)\n"
     ]
    }
   ],
   "source": [
    "# 05 - Darts ARIMA - Steady Train Size 30\n",
    "\n",
    "# Create confusion matrix per chunk by aggregating so that TP, FN, FP, and TN of the several chunk iterations are summed up\n",
    "\n",
    "confusion_matrix_per_chunk_05_darts_arima_30_hr = aggregate_to_confusion_matrix_per_chunk(confusion_matrices_per_chunk_05_darts_arima_30_hr)\n",
    "\n",
    "confusion_matrix_per_chunk_05_darts_arima_30_bp = aggregate_to_confusion_matrix_per_chunk(confusion_matrices_per_chunk_05_darts_arima_30_bp)\n",
    "\n",
    "confusion_matrix_per_chunk_05_darts_arima_30_o2 = aggregate_to_confusion_matrix_per_chunk(confusion_matrices_per_chunk_05_darts_arima_30_o2)\n",
    "\n",
    "# Create confusion matrix by aggregating so that TP, FN, FP, and TN of all chunks are summed up\n",
    "\n",
    "confusion_matrix_05_darts_arima_30_hr = aggregate_to_confusion_matrix_per_model(confusion_matrices_per_chunk_05_darts_arima_30_hr)\n",
    "\n",
    "confusion_matrix_05_darts_arima_30_bp = aggregate_to_confusion_matrix_per_model(confusion_matrices_per_chunk_05_darts_arima_30_bp)\n",
    "\n",
    "confusion_matrix_05_darts_arima_30_o2 = aggregate_to_confusion_matrix_per_model(confusion_matrices_per_chunk_05_darts_arima_30_o2)\n",
    "\n",
    "# Calculate statistical measures for evaluation of the model\n",
    "\n",
    "metrics_05_darts_arima_30_hr = calc_metrics(confusion_matrix_05_darts_arima_30_hr)\n",
    "\n",
    "metrics_05_darts_arima_30_bp = calc_metrics(confusion_matrix_05_darts_arima_30_bp)\n",
    "\n",
    "metrics_05_darts_arima_30_o2 = calc_metrics(confusion_matrix_05_darts_arima_30_o2)\n",
    "\n",
    "# Create overview table\n",
    "\n",
    "model_05_darts_arima_30 = pd.DataFrame({\n",
    "    'MODEL':            ['Darts ARIMA', 'Darts ARIMA'],\n",
    "    'RESAMPLING':       ['Median',      'Median'],\n",
    "    'EXOGENOUS':        ['n/a',         'n/a'],\n",
    "    'TRAINSIZE':        ['Steady',      'Steady'],\n",
    "    'FIRSTFORECAST':    ['30',          '30']\n",
    "    })\n",
    "\n",
    "overview_05_darts_arima_30_hr = pd.concat([\n",
    "    pd.DataFrame({'ID': ['HR_A_05_30_H', 'HR_A_05_30_L']}),\n",
    "    pd.DataFrame({'PARAMETER': ['HR']}),\n",
    "    model_05_darts_arima_30,\n",
    "    confusion_matrix_05_darts_arima_30_hr.reset_index(),\n",
    "    metrics_05_darts_arima_30_bp.loc[:, metrics_05_darts_arima_30_bp.columns != 'ALARMTYPE'],\n",
    "    stats_05_darts_arima_30_hr], axis=1).pad()\n",
    "\n",
    "overview_05_darts_arima_30_bp = pd.concat([\n",
    "    pd.DataFrame({'ID': ['BP_A_05_30_H', 'BP_A_05_30_L']}),\n",
    "    pd.DataFrame({'PARAMETER': ['BP']}),\n",
    "    model_05_darts_arima_30,\n",
    "    confusion_matrix_05_darts_arima_30_bp.reset_index(),\n",
    "    metrics_05_darts_arima_30_bp.loc[:, metrics_05_darts_arima_30_bp.columns != 'ALARMTYPE'],\n",
    "    stats_05_darts_arima_30_bp], axis=1).pad()\n",
    "\n",
    "overview_05_darts_arima_30_o2 = pd.concat([\n",
    "    pd.DataFrame({'ID': ['O2_A_05_30_H', 'O2_A_05_30_L']}),\n",
    "    pd.DataFrame({'PARAMETER': ['O2']}),\n",
    "    model_05_darts_arima_30,\n",
    "    confusion_matrix_05_darts_arima_30_o2.reset_index(),\n",
    "    metrics_05_darts_arima_30_o2.loc[:, metrics_05_darts_arima_30_o2.columns != 'ALARMTYPE'],\n",
    "    stats_05_darts_arima_30_o2], axis=1).pad()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "<ipython-input-11-0c679fe70579>:83: RuntimeWarning: invalid value encountered in longlong_scalars\n  tpr_high = TP_high/(TP_high+FN_high)\n<ipython-input-11-0c679fe70579>:84: RuntimeWarning: invalid value encountered in longlong_scalars\n  fnr_high = FN_high/(TP_high+FN_high)\n"
     ]
    }
   ],
   "source": [
    "# 06 - Darts ARIMA - Expanding Train Size 30\n",
    "\n",
    "# Create confusion matrix per chunk by aggregating so that TP, FN, FP, and TN of the several chunk iterations are summed up\n",
    "\n",
    "confusion_matrix_per_chunk_06_darts_arima_expanding_30_hr = aggregate_to_confusion_matrix_per_chunk(confusion_matrices_per_chunk_06_darts_arima_expanding_30_hr)\n",
    "\n",
    "confusion_matrix_per_chunk_06_darts_arima_expanding_30_bp = aggregate_to_confusion_matrix_per_chunk(confusion_matrices_per_chunk_06_darts_arima_expanding_30_bp)\n",
    "\n",
    "confusion_matrix_per_chunk_06_darts_arima_expanding_30_o2 = aggregate_to_confusion_matrix_per_chunk(confusion_matrices_per_chunk_06_darts_arima_expanding_30_o2)\n",
    "\n",
    "# Create confusion matrix by aggregating so that TP, FN, FP, and TN of all chunks are summed up\n",
    "\n",
    "confusion_matrix_06_darts_arima_expanding_30_hr = aggregate_to_confusion_matrix_per_model(confusion_matrices_per_chunk_06_darts_arima_expanding_30_hr)\n",
    "\n",
    "confusion_matrix_06_darts_arima_expanding_30_bp = aggregate_to_confusion_matrix_per_model(confusion_matrices_per_chunk_06_darts_arima_expanding_30_bp)\n",
    "\n",
    "confusion_matrix_06_darts_arima_expanding_30_o2 = aggregate_to_confusion_matrix_per_model(confusion_matrices_per_chunk_06_darts_arima_expanding_30_o2)\n",
    "\n",
    "# Calculate statistical measures for evaluation of the model\n",
    "\n",
    "metrics_06_darts_arima_expanding_30_hr = calc_metrics(confusion_matrix_06_darts_arima_expanding_30_hr)\n",
    "\n",
    "metrics_06_darts_arima_expanding_30_bp = calc_metrics(confusion_matrix_06_darts_arima_expanding_30_bp)\n",
    "\n",
    "metrics_06_darts_arima_expanding_30_o2 = calc_metrics(confusion_matrix_06_darts_arima_expanding_30_o2)\n",
    "\n",
    "# Create overview table\n",
    "\n",
    "model_06_darts_arima_expanding_30 = pd.DataFrame({\n",
    "    'MODEL':            ['Darts ARIMA', 'Darts ARIMA'],\n",
    "    'RESAMPLING':       ['Median',      'Median'],\n",
    "    'EXOGENOUS':        ['n/a',         'n/a'],\n",
    "    'TRAINSIZE':        ['Expanding',   'Expanding'],\n",
    "    'FIRSTFORECAST':    ['30',          '30']\n",
    "    })\n",
    "\n",
    "overview_06_darts_arima_expanding_30_hr = pd.concat([\n",
    "    pd.DataFrame({'ID': ['HR_A_06_30_H', 'HR_A_06_30_L']}),\n",
    "    pd.DataFrame({'PARAMETER': ['HR']}),\n",
    "    model_06_darts_arima_expanding_30,\n",
    "    confusion_matrix_06_darts_arima_expanding_30_hr.reset_index(),\n",
    "    metrics_06_darts_arima_expanding_30_bp.loc[:, metrics_06_darts_arima_expanding_30_bp.columns != 'ALARMTYPE'],\n",
    "    stats_06_darts_arima_expanding_30_hr], axis=1).pad()\n",
    "\n",
    "overview_06_darts_arima_expanding_30_bp = pd.concat([\n",
    "    pd.DataFrame({'ID': ['BP_A_06_30_H', 'BP_A_06_30_L']}),\n",
    "    pd.DataFrame({'PARAMETER': ['BP']}),\n",
    "    model_06_darts_arima_expanding_30,\n",
    "    confusion_matrix_06_darts_arima_expanding_30_bp.reset_index(),\n",
    "    metrics_06_darts_arima_expanding_30_bp.loc[:, metrics_06_darts_arima_expanding_30_bp.columns != 'ALARMTYPE'],\n",
    "    stats_06_darts_arima_expanding_30_bp], axis=1).pad()\n",
    "\n",
    "overview_06_darts_arima_expanding_30_o2 = pd.concat([\n",
    "    pd.DataFrame({'ID': ['O2_A_06_30_H', 'O2_A_06_30_L']}),\n",
    "    pd.DataFrame({'PARAMETER': ['O2']}),\n",
    "    model_06_darts_arima_expanding_30,\n",
    "    confusion_matrix_06_darts_arima_expanding_30_o2.reset_index(),\n",
    "    metrics_06_darts_arima_expanding_30_o2.loc[:, metrics_06_darts_arima_expanding_30_o2.columns != 'ALARMTYPE'],\n",
    "    stats_06_darts_arima_expanding_30_o2], axis=1).pad()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "<ipython-input-11-0c679fe70579>:83: RuntimeWarning: invalid value encountered in longlong_scalars\n  tpr_high = TP_high/(TP_high+FN_high)\n<ipython-input-11-0c679fe70579>:84: RuntimeWarning: invalid value encountered in longlong_scalars\n  fnr_high = FN_high/(TP_high+FN_high)\n"
     ]
    }
   ],
   "source": [
    "# 07 - Darts ARIMAX - Steady Train Size 30\n",
    "\n",
    "# Create confusion matrix per chunk by aggregating so that TP, FN, FP, and TN of the several chunk iterations are summed up\n",
    "\n",
    "confusion_matrix_per_chunk_07_darts_arimax_30_hr = aggregate_to_confusion_matrix_per_chunk(confusion_matrices_per_chunk_07_darts_arimax_30_hr)\n",
    "\n",
    "confusion_matrix_per_chunk_07_darts_arimax_30_bp = aggregate_to_confusion_matrix_per_chunk(confusion_matrices_per_chunk_07_darts_arimax_30_bp)\n",
    "\n",
    "confusion_matrix_per_chunk_07_darts_arimax_30_o2 = aggregate_to_confusion_matrix_per_chunk(confusion_matrices_per_chunk_07_darts_arimax_30_o2)\n",
    "\n",
    "# Create confusion matrix by aggregating so that TP, FN, FP, and TN of all chunks are summed up\n",
    "\n",
    "confusion_matrix_07_darts_arimax_30_hr = aggregate_to_confusion_matrix_per_model(confusion_matrices_per_chunk_07_darts_arimax_30_hr)\n",
    "\n",
    "confusion_matrix_07_darts_arimax_30_bp = aggregate_to_confusion_matrix_per_model(confusion_matrices_per_chunk_07_darts_arimax_30_bp)\n",
    "\n",
    "confusion_matrix_07_darts_arimax_30_o2 = aggregate_to_confusion_matrix_per_model(confusion_matrices_per_chunk_07_darts_arimax_30_o2)\n",
    "\n",
    "# Calculate statistical measures for evaluation of the model\n",
    "\n",
    "metrics_07_darts_arimax_30_hr = calc_metrics(confusion_matrix_07_darts_arimax_30_hr)\n",
    "\n",
    "metrics_07_darts_arimax_30_bp = calc_metrics(confusion_matrix_07_darts_arimax_30_bp)\n",
    "\n",
    "metrics_07_darts_arimax_30_o2 = calc_metrics(confusion_matrix_07_darts_arimax_30_o2)\n",
    "\n",
    "# Create overview table\n",
    "\n",
    "model_07_darts_arimax_30 = pd.DataFrame({\n",
    "    'MODEL':            ['Darts ARIMAX',    'Darts ARIMAX'],\n",
    "    'RESAMPLING':       ['Max',             'Min'],\n",
    "    'EXOGENOUS':        ['Median',          'Median'],\n",
    "    'TRAINSIZE':        ['Steady',          'Steady'],\n",
    "    'FIRSTFORECAST':    ['30',              '30']\n",
    "    })\n",
    "\n",
    "overview_07_darts_arimax_30_hr = pd.concat([\n",
    "    pd.DataFrame({'ID': ['HR_A_07_30_H', 'HR_A_07_30_L']}),\n",
    "    pd.DataFrame({'PARAMETER': ['HR']}),\n",
    "    model_07_darts_arimax_30,\n",
    "    confusion_matrix_07_darts_arimax_30_hr.reset_index(),\n",
    "    metrics_07_darts_arimax_30_bp.loc[:, metrics_07_darts_arimax_30_bp.columns != 'ALARMTYPE'],\n",
    "    stats_07_darts_arimax_30_hr], axis=1).pad()\n",
    "\n",
    "overview_07_darts_arimax_30_bp = pd.concat([\n",
    "    pd.DataFrame({'ID': ['BP_A_07_30_H', 'BP_A_07_30_L']}),\n",
    "    pd.DataFrame({'PARAMETER': ['BP']}),\n",
    "    model_07_darts_arimax_30,\n",
    "    confusion_matrix_07_darts_arimax_30_bp.reset_index(),\n",
    "    metrics_07_darts_arimax_30_bp.loc[:, metrics_07_darts_arimax_30_bp.columns != 'ALARMTYPE'],\n",
    "    stats_07_darts_arimax_30_bp], axis=1).pad()\n",
    "\n",
    "overview_07_darts_arimax_30_o2 = pd.concat([\n",
    "    pd.DataFrame({'ID': ['O2_A_07_30_H', 'O2_A_07_30_L']}),\n",
    "    pd.DataFrame({'PARAMETER': ['O2']}),\n",
    "    model_07_darts_arimax_30,\n",
    "    confusion_matrix_07_darts_arimax_30_o2.reset_index(),\n",
    "    metrics_07_darts_arimax_30_o2.loc[:, metrics_07_darts_arimax_30_o2.columns != 'ALARMTYPE'],\n",
    "    stats_07_darts_arimax_30_o2], axis=1).pad()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "<ipython-input-11-0c679fe70579>:83: RuntimeWarning: invalid value encountered in longlong_scalars\n  tpr_high = TP_high/(TP_high+FN_high)\n<ipython-input-11-0c679fe70579>:84: RuntimeWarning: invalid value encountered in longlong_scalars\n  fnr_high = FN_high/(TP_high+FN_high)\n"
     ]
    }
   ],
   "source": [
    "# 08 - Darts ARIMAX - Expanding Train Size 30\n",
    "\n",
    "# Create confusion matrix per chunk by aggregating so that TP, FN, FP, and TN of the several chunk iterations are summed up\n",
    "\n",
    "confusion_matrix_per_chunk_08_darts_arimax_expanding_30_hr = aggregate_to_confusion_matrix_per_chunk(confusion_matrices_per_chunk_08_darts_arimax_expanding_30_hr)\n",
    "\n",
    "confusion_matrix_per_chunk_08_darts_arimax_expanding_30_bp = aggregate_to_confusion_matrix_per_chunk(confusion_matrices_per_chunk_08_darts_arimax_expanding_30_bp)\n",
    "\n",
    "confusion_matrix_per_chunk_08_darts_arimax_expanding_30_o2 = aggregate_to_confusion_matrix_per_chunk(confusion_matrices_per_chunk_08_darts_arimax_expanding_30_o2)\n",
    "\n",
    "# Create confusion matrix by aggregating so that TP, FN, FP, and TN of all chunks are summed up\n",
    "\n",
    "confusion_matrix_08_darts_arimax_expanding_30_hr = aggregate_to_confusion_matrix_per_model(confusion_matrices_per_chunk_08_darts_arimax_expanding_30_hr)\n",
    "\n",
    "confusion_matrix_08_darts_arimax_expanding_30_bp = aggregate_to_confusion_matrix_per_model(confusion_matrices_per_chunk_08_darts_arimax_expanding_30_bp)\n",
    "\n",
    "confusion_matrix_08_darts_arimax_expanding_30_o2 = aggregate_to_confusion_matrix_per_model(confusion_matrices_per_chunk_08_darts_arimax_expanding_30_o2)\n",
    "\n",
    "# Calculate statistical measures for evaluation of the model\n",
    "\n",
    "metrics_08_darts_arimax_expanding_30_hr = calc_metrics(confusion_matrix_08_darts_arimax_expanding_30_hr)\n",
    "\n",
    "metrics_08_darts_arimax_expanding_30_bp = calc_metrics(confusion_matrix_08_darts_arimax_expanding_30_bp)\n",
    "\n",
    "metrics_08_darts_arimax_expanding_30_o2 = calc_metrics(confusion_matrix_08_darts_arimax_expanding_30_o2)\n",
    "\n",
    "# Create overview table\n",
    "\n",
    "model_08_darts_arimax_expanding_30 = pd.DataFrame({\n",
    "    'MODEL':            ['Darts ARIMAX',    'Darts ARIMAX'],\n",
    "    'RESAMPLING':       ['Max',             'Min'],\n",
    "    'EXOGENOUS':        ['Median',          'Median'],\n",
    "    'TRAINSIZE':        ['Expanding',       'Expanding'],\n",
    "    'FIRSTFORECAST':    ['30',              '30']\n",
    "    })\n",
    "\n",
    "overview_08_darts_arimax_expanding_30_hr = pd.concat([\n",
    "    pd.DataFrame({'ID': ['HR_A_08_30_H', 'HR_A_08_30_L']}),\n",
    "    pd.DataFrame({'PARAMETER': ['HR']}),\n",
    "    model_08_darts_arimax_expanding_30,\n",
    "    confusion_matrix_08_darts_arimax_expanding_30_hr.reset_index(),\n",
    "    metrics_08_darts_arimax_expanding_30_bp.loc[:, metrics_08_darts_arimax_expanding_30_bp.columns != 'ALARMTYPE'],\n",
    "    stats_08_darts_arimax_expanding_30_hr], axis=1).pad()\n",
    "\n",
    "overview_08_darts_arimax_expanding_30_bp = pd.concat([\n",
    "    pd.DataFrame({'ID': ['BP_A_08_30_H', 'BP_A_08_30_L']}),\n",
    "    pd.DataFrame({'PARAMETER': ['BP']}),\n",
    "    model_08_darts_arimax_expanding_30,\n",
    "    confusion_matrix_08_darts_arimax_expanding_30_bp.reset_index(),\n",
    "    metrics_08_darts_arimax_expanding_30_bp.loc[:, metrics_08_darts_arimax_expanding_30_bp.columns != 'ALARMTYPE'],\n",
    "    stats_08_darts_arimax_expanding_30_bp], axis=1).pad()\n",
    "\n",
    "overview_08_darts_arimax_expanding_30_o2 = pd.concat([\n",
    "    pd.DataFrame({'ID': ['O2_A_08_30_H', 'O2_A_08_30_L']}),\n",
    "    pd.DataFrame({'PARAMETER': ['O2']}),\n",
    "    model_08_darts_arimax_expanding_30,\n",
    "    confusion_matrix_08_darts_arimax_expanding_30_o2.reset_index(),\n",
    "    metrics_08_darts_arimax_expanding_30_o2.loc[:, metrics_08_darts_arimax_expanding_30_o2.columns != 'ALARMTYPE'],\n",
    "    stats_08_darts_arimax_expanding_30_o2], axis=1).pad()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create overview tables per parameter\n",
    "\n",
    "overview_hr = pd.concat([\n",
    "    overview_01_arima_steady_12_hr,\n",
    "    overview_01_arima_steady_30_hr,\n",
    "    overview_02_arima_expanding_12_hr,\n",
    "    overview_02_arima_expanding_30_hr,\n",
    "    overview_03_arimax_steady_12_hr,\n",
    "    overview_03_arimax_steady_30_hr,\n",
    "    overview_04_arimax_expanding_12_hr,\n",
    "    overview_04_arimax_expanding_30_hr,\n",
    "    overview_05_darts_arima_30_hr,\n",
    "    overview_06_darts_arima_expanding_30_hr,\n",
    "    overview_07_darts_arimax_30_hr,\n",
    "    overview_08_darts_arimax_expanding_30_hr], axis=0).reset_index(drop=True)\n",
    "\n",
    "overview_bp = pd.concat([\n",
    "    overview_01_arima_steady_12_bp,\n",
    "    overview_01_arima_steady_30_bp,\n",
    "    overview_02_arima_expanding_12_bp,\n",
    "    overview_02_arima_expanding_30_bp,\n",
    "    overview_03_arimax_steady_12_bp,\n",
    "    overview_03_arimax_steady_30_bp,\n",
    "    overview_04_arimax_expanding_12_bp,\n",
    "    overview_04_arimax_expanding_30_bp,\n",
    "    overview_05_darts_arima_30_bp,\n",
    "    overview_06_darts_arima_expanding_30_bp,\n",
    "    overview_07_darts_arimax_30_bp,\n",
    "    overview_08_darts_arimax_expanding_30_bp], axis=0).reset_index(drop=True)\n",
    "\n",
    "overview_o2 = pd.concat([\n",
    "    overview_01_arima_steady_12_o2,\n",
    "    overview_01_arima_steady_30_o2,\n",
    "    overview_02_arima_expanding_12_o2,\n",
    "    overview_02_arima_expanding_30_o2,\n",
    "    overview_03_arimax_steady_12_o2,\n",
    "    overview_03_arimax_steady_30_o2,\n",
    "    overview_04_arimax_expanding_12_o2,\n",
    "    overview_04_arimax_expanding_30_o2,\n",
    "    overview_05_darts_arima_30_o2,\n",
    "    overview_06_darts_arima_expanding_30_o2,\n",
    "    overview_07_darts_arimax_30_o2,\n",
    "    overview_08_darts_arimax_expanding_30_o2], axis=0).reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "              ID PARAMETER         MODEL RESAMPLING EXOGENOUS  TRAINSIZE  \\\n0   HR_A_01_12_H        HR         ARIMA     Median       n/a     Steady   \n1   HR_A_01_12_L        HR         ARIMA     Median       n/a     Steady   \n2   HR_A_01_30_H        HR         ARIMA     Median       n/a     Steady   \n3   HR_A_01_30_L        HR         ARIMA     Median       n/a     Steady   \n4   HR_A_02_12_H        HR         ARIMA     Median       n/a  Expanding   \n5   HR_A_02_12_L        HR         ARIMA     Median       n/a  Expanding   \n6   HR_A_02_30_H        HR         ARIMA     Median       n/a  Expanding   \n7   HR_A_02_30_L        HR         ARIMA     Median       n/a  Expanding   \n8   HR_A_03_12_H        HR        ARIMAX        Max    Median     Steady   \n9   HR_A_03_12_L        HR        ARIMAX        Min    Median     Steady   \n10  HR_A_03_30_H        HR        ARIMAX        Max    Median     Steady   \n11  HR_A_03_30_L        HR        ARIMAX        Min    Median     Steady   \n12  HR_A_04_12_H        HR        ARIMAX        Max    Median  Expanding   \n13  HR_A_04_12_L        HR        ARIMAX        Min    Median  Expanding   \n14  HR_A_04_30_H        HR        ARIMAX        Max    Median  Expanding   \n15  HR_A_04_30_L        HR        ARIMAX        Min    Median  Expanding   \n16  HR_A_05_30_H        HR   Darts ARIMA     Median       n/a     Steady   \n17  HR_A_05_30_L        HR   Darts ARIMA     Median       n/a     Steady   \n18  HR_A_06_30_H        HR   Darts ARIMA     Median       n/a  Expanding   \n19  HR_A_06_30_L        HR   Darts ARIMA     Median       n/a  Expanding   \n20  HR_A_07_30_H        HR  Darts ARIMAX        Max    Median     Steady   \n21  HR_A_07_30_L        HR  Darts ARIMAX        Min    Median     Steady   \n22  HR_A_08_30_H        HR  Darts ARIMAX        Max    Median  Expanding   \n23  HR_A_08_30_L        HR  Darts ARIMAX        Min    Median  Expanding   \n\n   FIRSTFORECAST ALARMTYPE   TP   FN   FP     TN     FPR     TPR     FNR  \\\n0             12      High  131  239  157  24584  0.0231  0.3545  0.6455   \n1             12       Low   96  101  220  24694  0.0111  0.2603  0.7397   \n2             30      High   93  153   76  15444  0.0071  0.0526  0.9474   \n3             30       Low   59   67  123  15517  0.0090  0.0870  0.9130   \n4             12      High  141  229  111  24630  0.0196  0.3545  0.6455   \n5             12       Low   95  102   88  24826  0.0099  0.1781  0.8219   \n6             30      High  100  146   65  15455  0.0125  0.0526  0.9474   \n7             30       Low   65   61   36  15604  0.0090  0.0435  0.9565   \n8             12      High  392   43   47  24630  0.0044  0.9310  0.0690   \n9             12       Low  194   13   25  24880  0.0052  0.9487  0.0513   \n10            30      High  261   35   47  15423  0.0036  0.9048  0.0952   \n11            30       Low  125    7    9  15625  0.0072  1.0000  0.0000   \n12            12      High  390   45   84  24593  0.0074  0.9310  0.0690   \n13            12       Low  193   14   36  24869  0.0052  0.9487  0.0513   \n14            30      High  265   31   56  15414  0.0054  0.9048  0.0952   \n15            30       Low  125    7   19  15615  0.0108  1.0000  0.0000   \n16            30      High   93  153   76  15444  0.0071  0.0526  0.9474   \n17            30       Low   59   67  122  15518  0.0090  0.0870  0.9130   \n18            30      High  101  145   64  15456  0.0107  0.0526  0.9474   \n19            30       Low   65   61   38  15602  0.0072  0.0435  0.9565   \n20            30      High  261   35   47  15423  0.0036  0.9048  0.0952   \n21            30       Low  125    7    9  15625  0.0072  1.0000  0.0000   \n22            30      High  265   31   56  15414  0.0054  0.9048  0.0952   \n23            30       Low  125    7   19  15615  0.0108  1.0000  0.0000   \n\n       TNR     ACC     F1S  CHUNKS  ITERATIONS  \n0   0.9769  0.9485  0.3861   671.0     25111.0  \n1   0.9889  0.9668  0.3220   671.0     25111.0  \n2   0.9929  0.9621  0.0833   374.0     15766.0  \n3   0.9910  0.9552  0.1333   374.0     15766.0  \n4   0.9804  0.9518  0.4021   671.0     25111.0  \n5   0.9901  0.9655  0.2385   671.0     25111.0  \n6   0.9875  0.9569  0.0741   374.0     15766.0  \n7   0.9910  0.9534  0.0690   374.0     15766.0  \n8   0.9956  0.9925  0.9231   671.0     25112.0  \n9   0.9948  0.9934  0.9024   671.0     25112.0  \n10  0.9964  0.9931  0.9048   374.0     15766.0  \n11  0.9928  0.9931  0.9231   374.0     15766.0  \n12  0.9926  0.9896  0.8963   671.0     25112.0  \n13  0.9948  0.9934  0.9024   671.0     25112.0  \n14  0.9946  0.9914  0.8837   374.0     15766.0  \n15  0.9892  0.9897  0.8889   374.0     15766.0  \n16  0.9929  0.9621  0.0833   374.0     15766.0  \n17  0.9910  0.9552  0.1333   374.0     15766.0  \n18  0.9893  0.9586  0.0769   374.0     15766.0  \n19  0.9928  0.9552  0.0714   374.0     15766.0  \n20  0.9964  0.9931  0.9048   374.0     15766.0  \n21  0.9928  0.9931  0.9231   374.0     15766.0  \n22  0.9946  0.9914  0.8837   374.0     15766.0  \n23  0.9892  0.9897  0.8889   374.0     15766.0  ",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>ID</th>\n      <th>PARAMETER</th>\n      <th>MODEL</th>\n      <th>RESAMPLING</th>\n      <th>EXOGENOUS</th>\n      <th>TRAINSIZE</th>\n      <th>FIRSTFORECAST</th>\n      <th>ALARMTYPE</th>\n      <th>TP</th>\n      <th>FN</th>\n      <th>FP</th>\n      <th>TN</th>\n      <th>FPR</th>\n      <th>TPR</th>\n      <th>FNR</th>\n      <th>TNR</th>\n      <th>ACC</th>\n      <th>F1S</th>\n      <th>CHUNKS</th>\n      <th>ITERATIONS</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>HR_A_01_12_H</td>\n      <td>HR</td>\n      <td>ARIMA</td>\n      <td>Median</td>\n      <td>n/a</td>\n      <td>Steady</td>\n      <td>12</td>\n      <td>High</td>\n      <td>131</td>\n      <td>239</td>\n      <td>157</td>\n      <td>24584</td>\n      <td>0.0231</td>\n      <td>0.3545</td>\n      <td>0.6455</td>\n      <td>0.9769</td>\n      <td>0.9485</td>\n      <td>0.3861</td>\n      <td>671.0</td>\n      <td>25111.0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>HR_A_01_12_L</td>\n      <td>HR</td>\n      <td>ARIMA</td>\n      <td>Median</td>\n      <td>n/a</td>\n      <td>Steady</td>\n      <td>12</td>\n      <td>Low</td>\n      <td>96</td>\n      <td>101</td>\n      <td>220</td>\n      <td>24694</td>\n      <td>0.0111</td>\n      <td>0.2603</td>\n      <td>0.7397</td>\n      <td>0.9889</td>\n      <td>0.9668</td>\n      <td>0.3220</td>\n      <td>671.0</td>\n      <td>25111.0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>HR_A_01_30_H</td>\n      <td>HR</td>\n      <td>ARIMA</td>\n      <td>Median</td>\n      <td>n/a</td>\n      <td>Steady</td>\n      <td>30</td>\n      <td>High</td>\n      <td>93</td>\n      <td>153</td>\n      <td>76</td>\n      <td>15444</td>\n      <td>0.0071</td>\n      <td>0.0526</td>\n      <td>0.9474</td>\n      <td>0.9929</td>\n      <td>0.9621</td>\n      <td>0.0833</td>\n      <td>374.0</td>\n      <td>15766.0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>HR_A_01_30_L</td>\n      <td>HR</td>\n      <td>ARIMA</td>\n      <td>Median</td>\n      <td>n/a</td>\n      <td>Steady</td>\n      <td>30</td>\n      <td>Low</td>\n      <td>59</td>\n      <td>67</td>\n      <td>123</td>\n      <td>15517</td>\n      <td>0.0090</td>\n      <td>0.0870</td>\n      <td>0.9130</td>\n      <td>0.9910</td>\n      <td>0.9552</td>\n      <td>0.1333</td>\n      <td>374.0</td>\n      <td>15766.0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>HR_A_02_12_H</td>\n      <td>HR</td>\n      <td>ARIMA</td>\n      <td>Median</td>\n      <td>n/a</td>\n      <td>Expanding</td>\n      <td>12</td>\n      <td>High</td>\n      <td>141</td>\n      <td>229</td>\n      <td>111</td>\n      <td>24630</td>\n      <td>0.0196</td>\n      <td>0.3545</td>\n      <td>0.6455</td>\n      <td>0.9804</td>\n      <td>0.9518</td>\n      <td>0.4021</td>\n      <td>671.0</td>\n      <td>25111.0</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>HR_A_02_12_L</td>\n      <td>HR</td>\n      <td>ARIMA</td>\n      <td>Median</td>\n      <td>n/a</td>\n      <td>Expanding</td>\n      <td>12</td>\n      <td>Low</td>\n      <td>95</td>\n      <td>102</td>\n      <td>88</td>\n      <td>24826</td>\n      <td>0.0099</td>\n      <td>0.1781</td>\n      <td>0.8219</td>\n      <td>0.9901</td>\n      <td>0.9655</td>\n      <td>0.2385</td>\n      <td>671.0</td>\n      <td>25111.0</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>HR_A_02_30_H</td>\n      <td>HR</td>\n      <td>ARIMA</td>\n      <td>Median</td>\n      <td>n/a</td>\n      <td>Expanding</td>\n      <td>30</td>\n      <td>High</td>\n      <td>100</td>\n      <td>146</td>\n      <td>65</td>\n      <td>15455</td>\n      <td>0.0125</td>\n      <td>0.0526</td>\n      <td>0.9474</td>\n      <td>0.9875</td>\n      <td>0.9569</td>\n      <td>0.0741</td>\n      <td>374.0</td>\n      <td>15766.0</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>HR_A_02_30_L</td>\n      <td>HR</td>\n      <td>ARIMA</td>\n      <td>Median</td>\n      <td>n/a</td>\n      <td>Expanding</td>\n      <td>30</td>\n      <td>Low</td>\n      <td>65</td>\n      <td>61</td>\n      <td>36</td>\n      <td>15604</td>\n      <td>0.0090</td>\n      <td>0.0435</td>\n      <td>0.9565</td>\n      <td>0.9910</td>\n      <td>0.9534</td>\n      <td>0.0690</td>\n      <td>374.0</td>\n      <td>15766.0</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>HR_A_03_12_H</td>\n      <td>HR</td>\n      <td>ARIMAX</td>\n      <td>Max</td>\n      <td>Median</td>\n      <td>Steady</td>\n      <td>12</td>\n      <td>High</td>\n      <td>392</td>\n      <td>43</td>\n      <td>47</td>\n      <td>24630</td>\n      <td>0.0044</td>\n      <td>0.9310</td>\n      <td>0.0690</td>\n      <td>0.9956</td>\n      <td>0.9925</td>\n      <td>0.9231</td>\n      <td>671.0</td>\n      <td>25112.0</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>HR_A_03_12_L</td>\n      <td>HR</td>\n      <td>ARIMAX</td>\n      <td>Min</td>\n      <td>Median</td>\n      <td>Steady</td>\n      <td>12</td>\n      <td>Low</td>\n      <td>194</td>\n      <td>13</td>\n      <td>25</td>\n      <td>24880</td>\n      <td>0.0052</td>\n      <td>0.9487</td>\n      <td>0.0513</td>\n      <td>0.9948</td>\n      <td>0.9934</td>\n      <td>0.9024</td>\n      <td>671.0</td>\n      <td>25112.0</td>\n    </tr>\n    <tr>\n      <th>10</th>\n      <td>HR_A_03_30_H</td>\n      <td>HR</td>\n      <td>ARIMAX</td>\n      <td>Max</td>\n      <td>Median</td>\n      <td>Steady</td>\n      <td>30</td>\n      <td>High</td>\n      <td>261</td>\n      <td>35</td>\n      <td>47</td>\n      <td>15423</td>\n      <td>0.0036</td>\n      <td>0.9048</td>\n      <td>0.0952</td>\n      <td>0.9964</td>\n      <td>0.9931</td>\n      <td>0.9048</td>\n      <td>374.0</td>\n      <td>15766.0</td>\n    </tr>\n    <tr>\n      <th>11</th>\n      <td>HR_A_03_30_L</td>\n      <td>HR</td>\n      <td>ARIMAX</td>\n      <td>Min</td>\n      <td>Median</td>\n      <td>Steady</td>\n      <td>30</td>\n      <td>Low</td>\n      <td>125</td>\n      <td>7</td>\n      <td>9</td>\n      <td>15625</td>\n      <td>0.0072</td>\n      <td>1.0000</td>\n      <td>0.0000</td>\n      <td>0.9928</td>\n      <td>0.9931</td>\n      <td>0.9231</td>\n      <td>374.0</td>\n      <td>15766.0</td>\n    </tr>\n    <tr>\n      <th>12</th>\n      <td>HR_A_04_12_H</td>\n      <td>HR</td>\n      <td>ARIMAX</td>\n      <td>Max</td>\n      <td>Median</td>\n      <td>Expanding</td>\n      <td>12</td>\n      <td>High</td>\n      <td>390</td>\n      <td>45</td>\n      <td>84</td>\n      <td>24593</td>\n      <td>0.0074</td>\n      <td>0.9310</td>\n      <td>0.0690</td>\n      <td>0.9926</td>\n      <td>0.9896</td>\n      <td>0.8963</td>\n      <td>671.0</td>\n      <td>25112.0</td>\n    </tr>\n    <tr>\n      <th>13</th>\n      <td>HR_A_04_12_L</td>\n      <td>HR</td>\n      <td>ARIMAX</td>\n      <td>Min</td>\n      <td>Median</td>\n      <td>Expanding</td>\n      <td>12</td>\n      <td>Low</td>\n      <td>193</td>\n      <td>14</td>\n      <td>36</td>\n      <td>24869</td>\n      <td>0.0052</td>\n      <td>0.9487</td>\n      <td>0.0513</td>\n      <td>0.9948</td>\n      <td>0.9934</td>\n      <td>0.9024</td>\n      <td>671.0</td>\n      <td>25112.0</td>\n    </tr>\n    <tr>\n      <th>14</th>\n      <td>HR_A_04_30_H</td>\n      <td>HR</td>\n      <td>ARIMAX</td>\n      <td>Max</td>\n      <td>Median</td>\n      <td>Expanding</td>\n      <td>30</td>\n      <td>High</td>\n      <td>265</td>\n      <td>31</td>\n      <td>56</td>\n      <td>15414</td>\n      <td>0.0054</td>\n      <td>0.9048</td>\n      <td>0.0952</td>\n      <td>0.9946</td>\n      <td>0.9914</td>\n      <td>0.8837</td>\n      <td>374.0</td>\n      <td>15766.0</td>\n    </tr>\n    <tr>\n      <th>15</th>\n      <td>HR_A_04_30_L</td>\n      <td>HR</td>\n      <td>ARIMAX</td>\n      <td>Min</td>\n      <td>Median</td>\n      <td>Expanding</td>\n      <td>30</td>\n      <td>Low</td>\n      <td>125</td>\n      <td>7</td>\n      <td>19</td>\n      <td>15615</td>\n      <td>0.0108</td>\n      <td>1.0000</td>\n      <td>0.0000</td>\n      <td>0.9892</td>\n      <td>0.9897</td>\n      <td>0.8889</td>\n      <td>374.0</td>\n      <td>15766.0</td>\n    </tr>\n    <tr>\n      <th>16</th>\n      <td>HR_A_05_30_H</td>\n      <td>HR</td>\n      <td>Darts ARIMA</td>\n      <td>Median</td>\n      <td>n/a</td>\n      <td>Steady</td>\n      <td>30</td>\n      <td>High</td>\n      <td>93</td>\n      <td>153</td>\n      <td>76</td>\n      <td>15444</td>\n      <td>0.0071</td>\n      <td>0.0526</td>\n      <td>0.9474</td>\n      <td>0.9929</td>\n      <td>0.9621</td>\n      <td>0.0833</td>\n      <td>374.0</td>\n      <td>15766.0</td>\n    </tr>\n    <tr>\n      <th>17</th>\n      <td>HR_A_05_30_L</td>\n      <td>HR</td>\n      <td>Darts ARIMA</td>\n      <td>Median</td>\n      <td>n/a</td>\n      <td>Steady</td>\n      <td>30</td>\n      <td>Low</td>\n      <td>59</td>\n      <td>67</td>\n      <td>122</td>\n      <td>15518</td>\n      <td>0.0090</td>\n      <td>0.0870</td>\n      <td>0.9130</td>\n      <td>0.9910</td>\n      <td>0.9552</td>\n      <td>0.1333</td>\n      <td>374.0</td>\n      <td>15766.0</td>\n    </tr>\n    <tr>\n      <th>18</th>\n      <td>HR_A_06_30_H</td>\n      <td>HR</td>\n      <td>Darts ARIMA</td>\n      <td>Median</td>\n      <td>n/a</td>\n      <td>Expanding</td>\n      <td>30</td>\n      <td>High</td>\n      <td>101</td>\n      <td>145</td>\n      <td>64</td>\n      <td>15456</td>\n      <td>0.0107</td>\n      <td>0.0526</td>\n      <td>0.9474</td>\n      <td>0.9893</td>\n      <td>0.9586</td>\n      <td>0.0769</td>\n      <td>374.0</td>\n      <td>15766.0</td>\n    </tr>\n    <tr>\n      <th>19</th>\n      <td>HR_A_06_30_L</td>\n      <td>HR</td>\n      <td>Darts ARIMA</td>\n      <td>Median</td>\n      <td>n/a</td>\n      <td>Expanding</td>\n      <td>30</td>\n      <td>Low</td>\n      <td>65</td>\n      <td>61</td>\n      <td>38</td>\n      <td>15602</td>\n      <td>0.0072</td>\n      <td>0.0435</td>\n      <td>0.9565</td>\n      <td>0.9928</td>\n      <td>0.9552</td>\n      <td>0.0714</td>\n      <td>374.0</td>\n      <td>15766.0</td>\n    </tr>\n    <tr>\n      <th>20</th>\n      <td>HR_A_07_30_H</td>\n      <td>HR</td>\n      <td>Darts ARIMAX</td>\n      <td>Max</td>\n      <td>Median</td>\n      <td>Steady</td>\n      <td>30</td>\n      <td>High</td>\n      <td>261</td>\n      <td>35</td>\n      <td>47</td>\n      <td>15423</td>\n      <td>0.0036</td>\n      <td>0.9048</td>\n      <td>0.0952</td>\n      <td>0.9964</td>\n      <td>0.9931</td>\n      <td>0.9048</td>\n      <td>374.0</td>\n      <td>15766.0</td>\n    </tr>\n    <tr>\n      <th>21</th>\n      <td>HR_A_07_30_L</td>\n      <td>HR</td>\n      <td>Darts ARIMAX</td>\n      <td>Min</td>\n      <td>Median</td>\n      <td>Steady</td>\n      <td>30</td>\n      <td>Low</td>\n      <td>125</td>\n      <td>7</td>\n      <td>9</td>\n      <td>15625</td>\n      <td>0.0072</td>\n      <td>1.0000</td>\n      <td>0.0000</td>\n      <td>0.9928</td>\n      <td>0.9931</td>\n      <td>0.9231</td>\n      <td>374.0</td>\n      <td>15766.0</td>\n    </tr>\n    <tr>\n      <th>22</th>\n      <td>HR_A_08_30_H</td>\n      <td>HR</td>\n      <td>Darts ARIMAX</td>\n      <td>Max</td>\n      <td>Median</td>\n      <td>Expanding</td>\n      <td>30</td>\n      <td>High</td>\n      <td>265</td>\n      <td>31</td>\n      <td>56</td>\n      <td>15414</td>\n      <td>0.0054</td>\n      <td>0.9048</td>\n      <td>0.0952</td>\n      <td>0.9946</td>\n      <td>0.9914</td>\n      <td>0.8837</td>\n      <td>374.0</td>\n      <td>15766.0</td>\n    </tr>\n    <tr>\n      <th>23</th>\n      <td>HR_A_08_30_L</td>\n      <td>HR</td>\n      <td>Darts ARIMAX</td>\n      <td>Min</td>\n      <td>Median</td>\n      <td>Expanding</td>\n      <td>30</td>\n      <td>Low</td>\n      <td>125</td>\n      <td>7</td>\n      <td>19</td>\n      <td>15615</td>\n      <td>0.0108</td>\n      <td>1.0000</td>\n      <td>0.0000</td>\n      <td>0.9892</td>\n      <td>0.9897</td>\n      <td>0.8889</td>\n      <td>374.0</td>\n      <td>15766.0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {}
    }
   ],
   "source": [
    "display(overview_hr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "              ID PARAMETER         MODEL RESAMPLING EXOGENOUS  TRAINSIZE  \\\n0   BP_A_01_12_H        BP         ARIMA     Median       n/a     Steady   \n1   BP_A_01_12_L        BP         ARIMA     Median       n/a     Steady   \n2   BP_A_01_30_H        BP         ARIMA     Median       n/a     Steady   \n3   BP_A_01_30_L        BP         ARIMA     Median       n/a     Steady   \n4   BP_A_02_12_H        BP         ARIMA     Median       n/a  Expanding   \n5   BP_A_02_12_L        BP         ARIMA     Median       n/a  Expanding   \n6   BP_A_02_30_H        BP         ARIMA     Median       n/a  Expanding   \n7   BP_A_02_30_L        BP         ARIMA     Median       n/a  Expanding   \n8   BP_A_03_12_H        BP        ARIMAX        Max    Median     Steady   \n9   BP_A_03_12_L        BP        ARIMAX        Min    Median     Steady   \n10  BP_A_03_30_H        BP        ARIMAX        Max    Median     Steady   \n11  BP_A_03_30_L        BP        ARIMAX        Min    Median     Steady   \n12  BP_A_04_12_H        BP        ARIMAX        Max    Median  Expanding   \n13  BP_A_04_12_L        BP        ARIMAX        Min    Median  Expanding   \n14  BP_A_04_30_H        BP        ARIMAX        Max    Median  Expanding   \n15  BP_A_04_30_L        BP        ARIMAX        Min    Median  Expanding   \n16  BP_A_05_30_H        BP   Darts ARIMA     Median       n/a     Steady   \n17  BP_A_05_30_L        BP   Darts ARIMA     Median       n/a     Steady   \n18  BP_A_06_30_H        BP   Darts ARIMA     Median       n/a  Expanding   \n19  BP_A_06_30_L        BP   Darts ARIMA     Median       n/a  Expanding   \n20  BP_A_07_30_H        BP  Darts ARIMAX        Max    Median     Steady   \n21  BP_A_07_30_L        BP  Darts ARIMAX        Min    Median     Steady   \n22  BP_A_08_30_H        BP  Darts ARIMAX        Max    Median  Expanding   \n23  BP_A_08_30_L        BP  Darts ARIMAX        Min    Median  Expanding   \n\n   FIRSTFORECAST ALARMTYPE   TP  FN  FP    TN     FPR     TPR     FNR     TNR  \\\n0             12      High   39  71  53  2244  0.0231  0.3545  0.6455  0.9769   \n1             12       Low   19  54  26  2308  0.0111  0.2603  0.7397  0.9889   \n2             30      High    1  18   4   557  0.0071  0.0526  0.9474  0.9929   \n3             30       Low    2  21   5   552  0.0090  0.0870  0.9130  0.9910   \n4             12      High   39  71  45  2251  0.0196  0.3545  0.6455  0.9804   \n5             12       Low   13  60  23  2310  0.0099  0.1781  0.8219  0.9901   \n6             30      High    1  18   7   554  0.0125  0.0526  0.9474  0.9875   \n7             30       Low    1  22   5   552  0.0090  0.0435  0.9565  0.9910   \n8             12      High  108   8  10  2281  0.0044  0.9310  0.0690  0.9956   \n9             12       Low   74   4  12  2317  0.0052  0.9487  0.0513  0.9948   \n10            30      High   19   2   2   557  0.0036  0.9048  0.0952  0.9964   \n11            30       Low   24   0   4   552  0.0072  1.0000  0.0000  0.9928   \n12            12      High  108   8  17  2274  0.0074  0.9310  0.0690  0.9926   \n13            12       Low   74   4  12  2317  0.0052  0.9487  0.0513  0.9948   \n14            30      High   19   2   3   556  0.0054  0.9048  0.0952  0.9946   \n15            30       Low   24   0   6   550  0.0108  1.0000  0.0000  0.9892   \n16            30      High    1  18   4   557  0.0071  0.0526  0.9474  0.9929   \n17            30       Low    2  21   5   552  0.0090  0.0870  0.9130  0.9910   \n18            30      High    1  18   6   555  0.0107  0.0526  0.9474  0.9893   \n19            30       Low    1  22   4   553  0.0072  0.0435  0.9565  0.9928   \n20            30      High   19   2   2   557  0.0036  0.9048  0.0952  0.9964   \n21            30       Low   24   0   4   552  0.0072  1.0000  0.0000  0.9928   \n22            30      High   19   2   3   556  0.0054  0.9048  0.0952  0.9946   \n23            30       Low   24   0   6   550  0.0108  1.0000  0.0000  0.9892   \n\n       ACC     F1S  CHUNKS  ITERATIONS  \n0   0.9485  0.3861   207.0      2407.0  \n1   0.9668  0.3220   207.0      2407.0  \n2   0.9621  0.0833    45.0       580.0  \n3   0.9552  0.1333    45.0       580.0  \n4   0.9518  0.4021   207.0      2406.0  \n5   0.9655  0.2385   207.0      2406.0  \n6   0.9569  0.0741    45.0       580.0  \n7   0.9534  0.0690    45.0       580.0  \n8   0.9925  0.9231   207.0      2407.0  \n9   0.9934  0.9024   207.0      2407.0  \n10  0.9931  0.9048    45.0       580.0  \n11  0.9931  0.9231    45.0       580.0  \n12  0.9896  0.8963   207.0      2407.0  \n13  0.9934  0.9024   207.0      2407.0  \n14  0.9914  0.8837    45.0       580.0  \n15  0.9897  0.8889    45.0       580.0  \n16  0.9621  0.0833    45.0       580.0  \n17  0.9552  0.1333    45.0       580.0  \n18  0.9586  0.0769    45.0       580.0  \n19  0.9552  0.0714    45.0       580.0  \n20  0.9931  0.9048    45.0       580.0  \n21  0.9931  0.9231    45.0       580.0  \n22  0.9914  0.8837    45.0       580.0  \n23  0.9897  0.8889    45.0       580.0  ",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>ID</th>\n      <th>PARAMETER</th>\n      <th>MODEL</th>\n      <th>RESAMPLING</th>\n      <th>EXOGENOUS</th>\n      <th>TRAINSIZE</th>\n      <th>FIRSTFORECAST</th>\n      <th>ALARMTYPE</th>\n      <th>TP</th>\n      <th>FN</th>\n      <th>FP</th>\n      <th>TN</th>\n      <th>FPR</th>\n      <th>TPR</th>\n      <th>FNR</th>\n      <th>TNR</th>\n      <th>ACC</th>\n      <th>F1S</th>\n      <th>CHUNKS</th>\n      <th>ITERATIONS</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>BP_A_01_12_H</td>\n      <td>BP</td>\n      <td>ARIMA</td>\n      <td>Median</td>\n      <td>n/a</td>\n      <td>Steady</td>\n      <td>12</td>\n      <td>High</td>\n      <td>39</td>\n      <td>71</td>\n      <td>53</td>\n      <td>2244</td>\n      <td>0.0231</td>\n      <td>0.3545</td>\n      <td>0.6455</td>\n      <td>0.9769</td>\n      <td>0.9485</td>\n      <td>0.3861</td>\n      <td>207.0</td>\n      <td>2407.0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>BP_A_01_12_L</td>\n      <td>BP</td>\n      <td>ARIMA</td>\n      <td>Median</td>\n      <td>n/a</td>\n      <td>Steady</td>\n      <td>12</td>\n      <td>Low</td>\n      <td>19</td>\n      <td>54</td>\n      <td>26</td>\n      <td>2308</td>\n      <td>0.0111</td>\n      <td>0.2603</td>\n      <td>0.7397</td>\n      <td>0.9889</td>\n      <td>0.9668</td>\n      <td>0.3220</td>\n      <td>207.0</td>\n      <td>2407.0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>BP_A_01_30_H</td>\n      <td>BP</td>\n      <td>ARIMA</td>\n      <td>Median</td>\n      <td>n/a</td>\n      <td>Steady</td>\n      <td>30</td>\n      <td>High</td>\n      <td>1</td>\n      <td>18</td>\n      <td>4</td>\n      <td>557</td>\n      <td>0.0071</td>\n      <td>0.0526</td>\n      <td>0.9474</td>\n      <td>0.9929</td>\n      <td>0.9621</td>\n      <td>0.0833</td>\n      <td>45.0</td>\n      <td>580.0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>BP_A_01_30_L</td>\n      <td>BP</td>\n      <td>ARIMA</td>\n      <td>Median</td>\n      <td>n/a</td>\n      <td>Steady</td>\n      <td>30</td>\n      <td>Low</td>\n      <td>2</td>\n      <td>21</td>\n      <td>5</td>\n      <td>552</td>\n      <td>0.0090</td>\n      <td>0.0870</td>\n      <td>0.9130</td>\n      <td>0.9910</td>\n      <td>0.9552</td>\n      <td>0.1333</td>\n      <td>45.0</td>\n      <td>580.0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>BP_A_02_12_H</td>\n      <td>BP</td>\n      <td>ARIMA</td>\n      <td>Median</td>\n      <td>n/a</td>\n      <td>Expanding</td>\n      <td>12</td>\n      <td>High</td>\n      <td>39</td>\n      <td>71</td>\n      <td>45</td>\n      <td>2251</td>\n      <td>0.0196</td>\n      <td>0.3545</td>\n      <td>0.6455</td>\n      <td>0.9804</td>\n      <td>0.9518</td>\n      <td>0.4021</td>\n      <td>207.0</td>\n      <td>2406.0</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>BP_A_02_12_L</td>\n      <td>BP</td>\n      <td>ARIMA</td>\n      <td>Median</td>\n      <td>n/a</td>\n      <td>Expanding</td>\n      <td>12</td>\n      <td>Low</td>\n      <td>13</td>\n      <td>60</td>\n      <td>23</td>\n      <td>2310</td>\n      <td>0.0099</td>\n      <td>0.1781</td>\n      <td>0.8219</td>\n      <td>0.9901</td>\n      <td>0.9655</td>\n      <td>0.2385</td>\n      <td>207.0</td>\n      <td>2406.0</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>BP_A_02_30_H</td>\n      <td>BP</td>\n      <td>ARIMA</td>\n      <td>Median</td>\n      <td>n/a</td>\n      <td>Expanding</td>\n      <td>30</td>\n      <td>High</td>\n      <td>1</td>\n      <td>18</td>\n      <td>7</td>\n      <td>554</td>\n      <td>0.0125</td>\n      <td>0.0526</td>\n      <td>0.9474</td>\n      <td>0.9875</td>\n      <td>0.9569</td>\n      <td>0.0741</td>\n      <td>45.0</td>\n      <td>580.0</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>BP_A_02_30_L</td>\n      <td>BP</td>\n      <td>ARIMA</td>\n      <td>Median</td>\n      <td>n/a</td>\n      <td>Expanding</td>\n      <td>30</td>\n      <td>Low</td>\n      <td>1</td>\n      <td>22</td>\n      <td>5</td>\n      <td>552</td>\n      <td>0.0090</td>\n      <td>0.0435</td>\n      <td>0.9565</td>\n      <td>0.9910</td>\n      <td>0.9534</td>\n      <td>0.0690</td>\n      <td>45.0</td>\n      <td>580.0</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>BP_A_03_12_H</td>\n      <td>BP</td>\n      <td>ARIMAX</td>\n      <td>Max</td>\n      <td>Median</td>\n      <td>Steady</td>\n      <td>12</td>\n      <td>High</td>\n      <td>108</td>\n      <td>8</td>\n      <td>10</td>\n      <td>2281</td>\n      <td>0.0044</td>\n      <td>0.9310</td>\n      <td>0.0690</td>\n      <td>0.9956</td>\n      <td>0.9925</td>\n      <td>0.9231</td>\n      <td>207.0</td>\n      <td>2407.0</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>BP_A_03_12_L</td>\n      <td>BP</td>\n      <td>ARIMAX</td>\n      <td>Min</td>\n      <td>Median</td>\n      <td>Steady</td>\n      <td>12</td>\n      <td>Low</td>\n      <td>74</td>\n      <td>4</td>\n      <td>12</td>\n      <td>2317</td>\n      <td>0.0052</td>\n      <td>0.9487</td>\n      <td>0.0513</td>\n      <td>0.9948</td>\n      <td>0.9934</td>\n      <td>0.9024</td>\n      <td>207.0</td>\n      <td>2407.0</td>\n    </tr>\n    <tr>\n      <th>10</th>\n      <td>BP_A_03_30_H</td>\n      <td>BP</td>\n      <td>ARIMAX</td>\n      <td>Max</td>\n      <td>Median</td>\n      <td>Steady</td>\n      <td>30</td>\n      <td>High</td>\n      <td>19</td>\n      <td>2</td>\n      <td>2</td>\n      <td>557</td>\n      <td>0.0036</td>\n      <td>0.9048</td>\n      <td>0.0952</td>\n      <td>0.9964</td>\n      <td>0.9931</td>\n      <td>0.9048</td>\n      <td>45.0</td>\n      <td>580.0</td>\n    </tr>\n    <tr>\n      <th>11</th>\n      <td>BP_A_03_30_L</td>\n      <td>BP</td>\n      <td>ARIMAX</td>\n      <td>Min</td>\n      <td>Median</td>\n      <td>Steady</td>\n      <td>30</td>\n      <td>Low</td>\n      <td>24</td>\n      <td>0</td>\n      <td>4</td>\n      <td>552</td>\n      <td>0.0072</td>\n      <td>1.0000</td>\n      <td>0.0000</td>\n      <td>0.9928</td>\n      <td>0.9931</td>\n      <td>0.9231</td>\n      <td>45.0</td>\n      <td>580.0</td>\n    </tr>\n    <tr>\n      <th>12</th>\n      <td>BP_A_04_12_H</td>\n      <td>BP</td>\n      <td>ARIMAX</td>\n      <td>Max</td>\n      <td>Median</td>\n      <td>Expanding</td>\n      <td>12</td>\n      <td>High</td>\n      <td>108</td>\n      <td>8</td>\n      <td>17</td>\n      <td>2274</td>\n      <td>0.0074</td>\n      <td>0.9310</td>\n      <td>0.0690</td>\n      <td>0.9926</td>\n      <td>0.9896</td>\n      <td>0.8963</td>\n      <td>207.0</td>\n      <td>2407.0</td>\n    </tr>\n    <tr>\n      <th>13</th>\n      <td>BP_A_04_12_L</td>\n      <td>BP</td>\n      <td>ARIMAX</td>\n      <td>Min</td>\n      <td>Median</td>\n      <td>Expanding</td>\n      <td>12</td>\n      <td>Low</td>\n      <td>74</td>\n      <td>4</td>\n      <td>12</td>\n      <td>2317</td>\n      <td>0.0052</td>\n      <td>0.9487</td>\n      <td>0.0513</td>\n      <td>0.9948</td>\n      <td>0.9934</td>\n      <td>0.9024</td>\n      <td>207.0</td>\n      <td>2407.0</td>\n    </tr>\n    <tr>\n      <th>14</th>\n      <td>BP_A_04_30_H</td>\n      <td>BP</td>\n      <td>ARIMAX</td>\n      <td>Max</td>\n      <td>Median</td>\n      <td>Expanding</td>\n      <td>30</td>\n      <td>High</td>\n      <td>19</td>\n      <td>2</td>\n      <td>3</td>\n      <td>556</td>\n      <td>0.0054</td>\n      <td>0.9048</td>\n      <td>0.0952</td>\n      <td>0.9946</td>\n      <td>0.9914</td>\n      <td>0.8837</td>\n      <td>45.0</td>\n      <td>580.0</td>\n    </tr>\n    <tr>\n      <th>15</th>\n      <td>BP_A_04_30_L</td>\n      <td>BP</td>\n      <td>ARIMAX</td>\n      <td>Min</td>\n      <td>Median</td>\n      <td>Expanding</td>\n      <td>30</td>\n      <td>Low</td>\n      <td>24</td>\n      <td>0</td>\n      <td>6</td>\n      <td>550</td>\n      <td>0.0108</td>\n      <td>1.0000</td>\n      <td>0.0000</td>\n      <td>0.9892</td>\n      <td>0.9897</td>\n      <td>0.8889</td>\n      <td>45.0</td>\n      <td>580.0</td>\n    </tr>\n    <tr>\n      <th>16</th>\n      <td>BP_A_05_30_H</td>\n      <td>BP</td>\n      <td>Darts ARIMA</td>\n      <td>Median</td>\n      <td>n/a</td>\n      <td>Steady</td>\n      <td>30</td>\n      <td>High</td>\n      <td>1</td>\n      <td>18</td>\n      <td>4</td>\n      <td>557</td>\n      <td>0.0071</td>\n      <td>0.0526</td>\n      <td>0.9474</td>\n      <td>0.9929</td>\n      <td>0.9621</td>\n      <td>0.0833</td>\n      <td>45.0</td>\n      <td>580.0</td>\n    </tr>\n    <tr>\n      <th>17</th>\n      <td>BP_A_05_30_L</td>\n      <td>BP</td>\n      <td>Darts ARIMA</td>\n      <td>Median</td>\n      <td>n/a</td>\n      <td>Steady</td>\n      <td>30</td>\n      <td>Low</td>\n      <td>2</td>\n      <td>21</td>\n      <td>5</td>\n      <td>552</td>\n      <td>0.0090</td>\n      <td>0.0870</td>\n      <td>0.9130</td>\n      <td>0.9910</td>\n      <td>0.9552</td>\n      <td>0.1333</td>\n      <td>45.0</td>\n      <td>580.0</td>\n    </tr>\n    <tr>\n      <th>18</th>\n      <td>BP_A_06_30_H</td>\n      <td>BP</td>\n      <td>Darts ARIMA</td>\n      <td>Median</td>\n      <td>n/a</td>\n      <td>Expanding</td>\n      <td>30</td>\n      <td>High</td>\n      <td>1</td>\n      <td>18</td>\n      <td>6</td>\n      <td>555</td>\n      <td>0.0107</td>\n      <td>0.0526</td>\n      <td>0.9474</td>\n      <td>0.9893</td>\n      <td>0.9586</td>\n      <td>0.0769</td>\n      <td>45.0</td>\n      <td>580.0</td>\n    </tr>\n    <tr>\n      <th>19</th>\n      <td>BP_A_06_30_L</td>\n      <td>BP</td>\n      <td>Darts ARIMA</td>\n      <td>Median</td>\n      <td>n/a</td>\n      <td>Expanding</td>\n      <td>30</td>\n      <td>Low</td>\n      <td>1</td>\n      <td>22</td>\n      <td>4</td>\n      <td>553</td>\n      <td>0.0072</td>\n      <td>0.0435</td>\n      <td>0.9565</td>\n      <td>0.9928</td>\n      <td>0.9552</td>\n      <td>0.0714</td>\n      <td>45.0</td>\n      <td>580.0</td>\n    </tr>\n    <tr>\n      <th>20</th>\n      <td>BP_A_07_30_H</td>\n      <td>BP</td>\n      <td>Darts ARIMAX</td>\n      <td>Max</td>\n      <td>Median</td>\n      <td>Steady</td>\n      <td>30</td>\n      <td>High</td>\n      <td>19</td>\n      <td>2</td>\n      <td>2</td>\n      <td>557</td>\n      <td>0.0036</td>\n      <td>0.9048</td>\n      <td>0.0952</td>\n      <td>0.9964</td>\n      <td>0.9931</td>\n      <td>0.9048</td>\n      <td>45.0</td>\n      <td>580.0</td>\n    </tr>\n    <tr>\n      <th>21</th>\n      <td>BP_A_07_30_L</td>\n      <td>BP</td>\n      <td>Darts ARIMAX</td>\n      <td>Min</td>\n      <td>Median</td>\n      <td>Steady</td>\n      <td>30</td>\n      <td>Low</td>\n      <td>24</td>\n      <td>0</td>\n      <td>4</td>\n      <td>552</td>\n      <td>0.0072</td>\n      <td>1.0000</td>\n      <td>0.0000</td>\n      <td>0.9928</td>\n      <td>0.9931</td>\n      <td>0.9231</td>\n      <td>45.0</td>\n      <td>580.0</td>\n    </tr>\n    <tr>\n      <th>22</th>\n      <td>BP_A_08_30_H</td>\n      <td>BP</td>\n      <td>Darts ARIMAX</td>\n      <td>Max</td>\n      <td>Median</td>\n      <td>Expanding</td>\n      <td>30</td>\n      <td>High</td>\n      <td>19</td>\n      <td>2</td>\n      <td>3</td>\n      <td>556</td>\n      <td>0.0054</td>\n      <td>0.9048</td>\n      <td>0.0952</td>\n      <td>0.9946</td>\n      <td>0.9914</td>\n      <td>0.8837</td>\n      <td>45.0</td>\n      <td>580.0</td>\n    </tr>\n    <tr>\n      <th>23</th>\n      <td>BP_A_08_30_L</td>\n      <td>BP</td>\n      <td>Darts ARIMAX</td>\n      <td>Min</td>\n      <td>Median</td>\n      <td>Expanding</td>\n      <td>30</td>\n      <td>Low</td>\n      <td>24</td>\n      <td>0</td>\n      <td>6</td>\n      <td>550</td>\n      <td>0.0108</td>\n      <td>1.0000</td>\n      <td>0.0000</td>\n      <td>0.9892</td>\n      <td>0.9897</td>\n      <td>0.8889</td>\n      <td>45.0</td>\n      <td>580.0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {}
    }
   ],
   "source": [
    "display(overview_bp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "              ID PARAMETER         MODEL RESAMPLING EXOGENOUS  TRAINSIZE  \\\n0   O2_A_01_12_H        O2         ARIMA     Median       n/a     Steady   \n1   O2_A_01_12_L        O2         ARIMA     Median       n/a     Steady   \n2   O2_A_01_30_H        O2         ARIMA     Median       n/a     Steady   \n3   O2_A_01_30_L        O2         ARIMA     Median       n/a     Steady   \n4   O2_A_02_12_H        O2         ARIMA     Median       n/a  Expanding   \n5   O2_A_02_12_L        O2         ARIMA     Median       n/a  Expanding   \n6   O2_A_02_30_H        O2         ARIMA     Median       n/a  Expanding   \n7   O2_A_02_30_L        O2         ARIMA     Median       n/a  Expanding   \n8   O2_A_03_12_H        O2        ARIMAX        Max    Median     Steady   \n9   O2_A_03_12_L        O2        ARIMAX        Min    Median     Steady   \n10  O2_A_03_30_H        O2        ARIMAX        Max    Median     Steady   \n11  O2_A_03_30_L        O2        ARIMAX        Min    Median     Steady   \n12  O2_A_04_12_H        O2        ARIMAX        Max    Median  Expanding   \n13  O2_A_04_12_L        O2        ARIMAX        Min    Median  Expanding   \n14  O2_A_04_30_H        O2        ARIMAX        Max    Median  Expanding   \n15  O2_A_04_30_L        O2        ARIMAX        Min    Median  Expanding   \n16  O2_A_05_30_H        O2   Darts ARIMA     Median       n/a     Steady   \n17  O2_A_05_30_L        O2   Darts ARIMA     Median       n/a     Steady   \n18  O2_A_06_30_H        O2   Darts ARIMA     Median       n/a  Expanding   \n19  O2_A_06_30_L        O2   Darts ARIMA     Median       n/a  Expanding   \n20  O2_A_07_30_H        O2  Darts ARIMAX        Max    Median     Steady   \n21  O2_A_07_30_L        O2  Darts ARIMAX        Min    Median     Steady   \n22  O2_A_08_30_H        O2  Darts ARIMAX        Max    Median  Expanding   \n23  O2_A_08_30_L        O2  Darts ARIMAX        Min    Median  Expanding   \n\n   FIRSTFORECAST ALARMTYPE   TP   FN   FP     TN     FPR     TPR     FNR  \\\n0             12      High   18    0  102  11595  0.0087  1.0000  0.0000   \n1             12       Low   65  163  446  11041  0.0388  0.2851  0.7149   \n2             30      High    0    0   25   6221  0.0040     NaN     NaN   \n3             30       Low   26   95   94   6031  0.0153  0.2149  0.7851   \n4             12      High   18    0   86  11612  0.0074  1.0000  0.0000   \n5             12       Low   60  168  196  11292  0.0171  0.2632  0.7368   \n6             30      High    0    0   40   6205  0.0064     NaN     NaN   \n7             30       Low   20  101   55   6069  0.0090  0.1653  0.8347   \n8             12      High   18    0  354  11345  0.0303  1.0000  0.0000   \n9             12       Low  241   30   84  11362  0.0073  0.8893  0.1107   \n10            30      High    0    0  305   5941  0.0488     NaN     NaN   \n11            30       Low  127   19   58   6042  0.0095  0.8699  0.1301   \n12            12      High   18    0  763  10936  0.0652  1.0000  0.0000   \n13            12       Low  244   27  141  11305  0.0123  0.9004  0.0996   \n14            30      High    0    0  453   5793  0.0725     NaN     NaN   \n15            30       Low  130   16   74   6026  0.0121  0.8904  0.1096   \n16            30      High    0    0   24   6222  0.0038     NaN     NaN   \n17            30       Low   25   96   90   6035  0.0147  0.2066  0.7934   \n18            30      High    0    0   33   6213  0.0053     NaN     NaN   \n19            30       Low   21  100   52   6073  0.0085  0.1736  0.8264   \n20            30      High    0    0  305   5941  0.0488     NaN     NaN   \n21            30       Low  127   19   58   6042  0.0095  0.8699  0.1301   \n22            30      High    0    0  453   5793  0.0725     NaN     NaN   \n23            30       Low  130   16   74   6026  0.0121  0.8904  0.1096   \n\n       TNR     ACC     F1S  CHUNKS  ITERATIONS  \n0   0.9913  0.9913  0.2609   438.0     11715.0  \n1   0.9612  0.9480  0.1759   438.0     11715.0  \n2   0.9960  0.9960  0.0000   188.0      6246.0  \n3   0.9847  0.9697  0.2158   188.0      6246.0  \n4   0.9926  0.9927  0.2951   438.0     11716.0  \n5   0.9829  0.9689  0.2479   438.0     11716.0  \n6   0.9936  0.9936  0.0000   188.0      6245.0  \n7   0.9910  0.9750  0.2041   188.0      6245.0  \n8   0.9697  0.9698  0.0923   438.0     11717.0  \n9   0.9927  0.9903  0.8087   438.0     11717.0  \n10  0.9512  0.9512  0.0000   188.0      6246.0  \n11  0.9905  0.9877  0.7674   188.0      6246.0  \n12  0.9348  0.9349  0.0451   438.0     11717.0  \n13  0.9877  0.9857  0.7439   438.0     11717.0  \n14  0.9275  0.9275  0.0000   188.0      6246.0  \n15  0.9879  0.9856  0.7429   188.0      6246.0  \n16  0.9962  0.9962  0.0000   188.0      6246.0  \n17  0.9853  0.9702  0.2119   188.0      6246.0  \n18  0.9947  0.9947  0.0000   188.0      6246.0  \n19  0.9915  0.9757  0.2165   188.0      6246.0  \n20  0.9512  0.9512  0.0000   188.0      6246.0  \n21  0.9905  0.9877  0.7674   188.0      6246.0  \n22  0.9275  0.9275  0.0000   188.0      6246.0  \n23  0.9879  0.9856  0.7429   188.0      6246.0  ",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>ID</th>\n      <th>PARAMETER</th>\n      <th>MODEL</th>\n      <th>RESAMPLING</th>\n      <th>EXOGENOUS</th>\n      <th>TRAINSIZE</th>\n      <th>FIRSTFORECAST</th>\n      <th>ALARMTYPE</th>\n      <th>TP</th>\n      <th>FN</th>\n      <th>FP</th>\n      <th>TN</th>\n      <th>FPR</th>\n      <th>TPR</th>\n      <th>FNR</th>\n      <th>TNR</th>\n      <th>ACC</th>\n      <th>F1S</th>\n      <th>CHUNKS</th>\n      <th>ITERATIONS</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>O2_A_01_12_H</td>\n      <td>O2</td>\n      <td>ARIMA</td>\n      <td>Median</td>\n      <td>n/a</td>\n      <td>Steady</td>\n      <td>12</td>\n      <td>High</td>\n      <td>18</td>\n      <td>0</td>\n      <td>102</td>\n      <td>11595</td>\n      <td>0.0087</td>\n      <td>1.0000</td>\n      <td>0.0000</td>\n      <td>0.9913</td>\n      <td>0.9913</td>\n      <td>0.2609</td>\n      <td>438.0</td>\n      <td>11715.0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>O2_A_01_12_L</td>\n      <td>O2</td>\n      <td>ARIMA</td>\n      <td>Median</td>\n      <td>n/a</td>\n      <td>Steady</td>\n      <td>12</td>\n      <td>Low</td>\n      <td>65</td>\n      <td>163</td>\n      <td>446</td>\n      <td>11041</td>\n      <td>0.0388</td>\n      <td>0.2851</td>\n      <td>0.7149</td>\n      <td>0.9612</td>\n      <td>0.9480</td>\n      <td>0.1759</td>\n      <td>438.0</td>\n      <td>11715.0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>O2_A_01_30_H</td>\n      <td>O2</td>\n      <td>ARIMA</td>\n      <td>Median</td>\n      <td>n/a</td>\n      <td>Steady</td>\n      <td>30</td>\n      <td>High</td>\n      <td>0</td>\n      <td>0</td>\n      <td>25</td>\n      <td>6221</td>\n      <td>0.0040</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.9960</td>\n      <td>0.9960</td>\n      <td>0.0000</td>\n      <td>188.0</td>\n      <td>6246.0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>O2_A_01_30_L</td>\n      <td>O2</td>\n      <td>ARIMA</td>\n      <td>Median</td>\n      <td>n/a</td>\n      <td>Steady</td>\n      <td>30</td>\n      <td>Low</td>\n      <td>26</td>\n      <td>95</td>\n      <td>94</td>\n      <td>6031</td>\n      <td>0.0153</td>\n      <td>0.2149</td>\n      <td>0.7851</td>\n      <td>0.9847</td>\n      <td>0.9697</td>\n      <td>0.2158</td>\n      <td>188.0</td>\n      <td>6246.0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>O2_A_02_12_H</td>\n      <td>O2</td>\n      <td>ARIMA</td>\n      <td>Median</td>\n      <td>n/a</td>\n      <td>Expanding</td>\n      <td>12</td>\n      <td>High</td>\n      <td>18</td>\n      <td>0</td>\n      <td>86</td>\n      <td>11612</td>\n      <td>0.0074</td>\n      <td>1.0000</td>\n      <td>0.0000</td>\n      <td>0.9926</td>\n      <td>0.9927</td>\n      <td>0.2951</td>\n      <td>438.0</td>\n      <td>11716.0</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>O2_A_02_12_L</td>\n      <td>O2</td>\n      <td>ARIMA</td>\n      <td>Median</td>\n      <td>n/a</td>\n      <td>Expanding</td>\n      <td>12</td>\n      <td>Low</td>\n      <td>60</td>\n      <td>168</td>\n      <td>196</td>\n      <td>11292</td>\n      <td>0.0171</td>\n      <td>0.2632</td>\n      <td>0.7368</td>\n      <td>0.9829</td>\n      <td>0.9689</td>\n      <td>0.2479</td>\n      <td>438.0</td>\n      <td>11716.0</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>O2_A_02_30_H</td>\n      <td>O2</td>\n      <td>ARIMA</td>\n      <td>Median</td>\n      <td>n/a</td>\n      <td>Expanding</td>\n      <td>30</td>\n      <td>High</td>\n      <td>0</td>\n      <td>0</td>\n      <td>40</td>\n      <td>6205</td>\n      <td>0.0064</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.9936</td>\n      <td>0.9936</td>\n      <td>0.0000</td>\n      <td>188.0</td>\n      <td>6245.0</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>O2_A_02_30_L</td>\n      <td>O2</td>\n      <td>ARIMA</td>\n      <td>Median</td>\n      <td>n/a</td>\n      <td>Expanding</td>\n      <td>30</td>\n      <td>Low</td>\n      <td>20</td>\n      <td>101</td>\n      <td>55</td>\n      <td>6069</td>\n      <td>0.0090</td>\n      <td>0.1653</td>\n      <td>0.8347</td>\n      <td>0.9910</td>\n      <td>0.9750</td>\n      <td>0.2041</td>\n      <td>188.0</td>\n      <td>6245.0</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>O2_A_03_12_H</td>\n      <td>O2</td>\n      <td>ARIMAX</td>\n      <td>Max</td>\n      <td>Median</td>\n      <td>Steady</td>\n      <td>12</td>\n      <td>High</td>\n      <td>18</td>\n      <td>0</td>\n      <td>354</td>\n      <td>11345</td>\n      <td>0.0303</td>\n      <td>1.0000</td>\n      <td>0.0000</td>\n      <td>0.9697</td>\n      <td>0.9698</td>\n      <td>0.0923</td>\n      <td>438.0</td>\n      <td>11717.0</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>O2_A_03_12_L</td>\n      <td>O2</td>\n      <td>ARIMAX</td>\n      <td>Min</td>\n      <td>Median</td>\n      <td>Steady</td>\n      <td>12</td>\n      <td>Low</td>\n      <td>241</td>\n      <td>30</td>\n      <td>84</td>\n      <td>11362</td>\n      <td>0.0073</td>\n      <td>0.8893</td>\n      <td>0.1107</td>\n      <td>0.9927</td>\n      <td>0.9903</td>\n      <td>0.8087</td>\n      <td>438.0</td>\n      <td>11717.0</td>\n    </tr>\n    <tr>\n      <th>10</th>\n      <td>O2_A_03_30_H</td>\n      <td>O2</td>\n      <td>ARIMAX</td>\n      <td>Max</td>\n      <td>Median</td>\n      <td>Steady</td>\n      <td>30</td>\n      <td>High</td>\n      <td>0</td>\n      <td>0</td>\n      <td>305</td>\n      <td>5941</td>\n      <td>0.0488</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.9512</td>\n      <td>0.9512</td>\n      <td>0.0000</td>\n      <td>188.0</td>\n      <td>6246.0</td>\n    </tr>\n    <tr>\n      <th>11</th>\n      <td>O2_A_03_30_L</td>\n      <td>O2</td>\n      <td>ARIMAX</td>\n      <td>Min</td>\n      <td>Median</td>\n      <td>Steady</td>\n      <td>30</td>\n      <td>Low</td>\n      <td>127</td>\n      <td>19</td>\n      <td>58</td>\n      <td>6042</td>\n      <td>0.0095</td>\n      <td>0.8699</td>\n      <td>0.1301</td>\n      <td>0.9905</td>\n      <td>0.9877</td>\n      <td>0.7674</td>\n      <td>188.0</td>\n      <td>6246.0</td>\n    </tr>\n    <tr>\n      <th>12</th>\n      <td>O2_A_04_12_H</td>\n      <td>O2</td>\n      <td>ARIMAX</td>\n      <td>Max</td>\n      <td>Median</td>\n      <td>Expanding</td>\n      <td>12</td>\n      <td>High</td>\n      <td>18</td>\n      <td>0</td>\n      <td>763</td>\n      <td>10936</td>\n      <td>0.0652</td>\n      <td>1.0000</td>\n      <td>0.0000</td>\n      <td>0.9348</td>\n      <td>0.9349</td>\n      <td>0.0451</td>\n      <td>438.0</td>\n      <td>11717.0</td>\n    </tr>\n    <tr>\n      <th>13</th>\n      <td>O2_A_04_12_L</td>\n      <td>O2</td>\n      <td>ARIMAX</td>\n      <td>Min</td>\n      <td>Median</td>\n      <td>Expanding</td>\n      <td>12</td>\n      <td>Low</td>\n      <td>244</td>\n      <td>27</td>\n      <td>141</td>\n      <td>11305</td>\n      <td>0.0123</td>\n      <td>0.9004</td>\n      <td>0.0996</td>\n      <td>0.9877</td>\n      <td>0.9857</td>\n      <td>0.7439</td>\n      <td>438.0</td>\n      <td>11717.0</td>\n    </tr>\n    <tr>\n      <th>14</th>\n      <td>O2_A_04_30_H</td>\n      <td>O2</td>\n      <td>ARIMAX</td>\n      <td>Max</td>\n      <td>Median</td>\n      <td>Expanding</td>\n      <td>30</td>\n      <td>High</td>\n      <td>0</td>\n      <td>0</td>\n      <td>453</td>\n      <td>5793</td>\n      <td>0.0725</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.9275</td>\n      <td>0.9275</td>\n      <td>0.0000</td>\n      <td>188.0</td>\n      <td>6246.0</td>\n    </tr>\n    <tr>\n      <th>15</th>\n      <td>O2_A_04_30_L</td>\n      <td>O2</td>\n      <td>ARIMAX</td>\n      <td>Min</td>\n      <td>Median</td>\n      <td>Expanding</td>\n      <td>30</td>\n      <td>Low</td>\n      <td>130</td>\n      <td>16</td>\n      <td>74</td>\n      <td>6026</td>\n      <td>0.0121</td>\n      <td>0.8904</td>\n      <td>0.1096</td>\n      <td>0.9879</td>\n      <td>0.9856</td>\n      <td>0.7429</td>\n      <td>188.0</td>\n      <td>6246.0</td>\n    </tr>\n    <tr>\n      <th>16</th>\n      <td>O2_A_05_30_H</td>\n      <td>O2</td>\n      <td>Darts ARIMA</td>\n      <td>Median</td>\n      <td>n/a</td>\n      <td>Steady</td>\n      <td>30</td>\n      <td>High</td>\n      <td>0</td>\n      <td>0</td>\n      <td>24</td>\n      <td>6222</td>\n      <td>0.0038</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.9962</td>\n      <td>0.9962</td>\n      <td>0.0000</td>\n      <td>188.0</td>\n      <td>6246.0</td>\n    </tr>\n    <tr>\n      <th>17</th>\n      <td>O2_A_05_30_L</td>\n      <td>O2</td>\n      <td>Darts ARIMA</td>\n      <td>Median</td>\n      <td>n/a</td>\n      <td>Steady</td>\n      <td>30</td>\n      <td>Low</td>\n      <td>25</td>\n      <td>96</td>\n      <td>90</td>\n      <td>6035</td>\n      <td>0.0147</td>\n      <td>0.2066</td>\n      <td>0.7934</td>\n      <td>0.9853</td>\n      <td>0.9702</td>\n      <td>0.2119</td>\n      <td>188.0</td>\n      <td>6246.0</td>\n    </tr>\n    <tr>\n      <th>18</th>\n      <td>O2_A_06_30_H</td>\n      <td>O2</td>\n      <td>Darts ARIMA</td>\n      <td>Median</td>\n      <td>n/a</td>\n      <td>Expanding</td>\n      <td>30</td>\n      <td>High</td>\n      <td>0</td>\n      <td>0</td>\n      <td>33</td>\n      <td>6213</td>\n      <td>0.0053</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.9947</td>\n      <td>0.9947</td>\n      <td>0.0000</td>\n      <td>188.0</td>\n      <td>6246.0</td>\n    </tr>\n    <tr>\n      <th>19</th>\n      <td>O2_A_06_30_L</td>\n      <td>O2</td>\n      <td>Darts ARIMA</td>\n      <td>Median</td>\n      <td>n/a</td>\n      <td>Expanding</td>\n      <td>30</td>\n      <td>Low</td>\n      <td>21</td>\n      <td>100</td>\n      <td>52</td>\n      <td>6073</td>\n      <td>0.0085</td>\n      <td>0.1736</td>\n      <td>0.8264</td>\n      <td>0.9915</td>\n      <td>0.9757</td>\n      <td>0.2165</td>\n      <td>188.0</td>\n      <td>6246.0</td>\n    </tr>\n    <tr>\n      <th>20</th>\n      <td>O2_A_07_30_H</td>\n      <td>O2</td>\n      <td>Darts ARIMAX</td>\n      <td>Max</td>\n      <td>Median</td>\n      <td>Steady</td>\n      <td>30</td>\n      <td>High</td>\n      <td>0</td>\n      <td>0</td>\n      <td>305</td>\n      <td>5941</td>\n      <td>0.0488</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.9512</td>\n      <td>0.9512</td>\n      <td>0.0000</td>\n      <td>188.0</td>\n      <td>6246.0</td>\n    </tr>\n    <tr>\n      <th>21</th>\n      <td>O2_A_07_30_L</td>\n      <td>O2</td>\n      <td>Darts ARIMAX</td>\n      <td>Min</td>\n      <td>Median</td>\n      <td>Steady</td>\n      <td>30</td>\n      <td>Low</td>\n      <td>127</td>\n      <td>19</td>\n      <td>58</td>\n      <td>6042</td>\n      <td>0.0095</td>\n      <td>0.8699</td>\n      <td>0.1301</td>\n      <td>0.9905</td>\n      <td>0.9877</td>\n      <td>0.7674</td>\n      <td>188.0</td>\n      <td>6246.0</td>\n    </tr>\n    <tr>\n      <th>22</th>\n      <td>O2_A_08_30_H</td>\n      <td>O2</td>\n      <td>Darts ARIMAX</td>\n      <td>Max</td>\n      <td>Median</td>\n      <td>Expanding</td>\n      <td>30</td>\n      <td>High</td>\n      <td>0</td>\n      <td>0</td>\n      <td>453</td>\n      <td>5793</td>\n      <td>0.0725</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.9275</td>\n      <td>0.9275</td>\n      <td>0.0000</td>\n      <td>188.0</td>\n      <td>6246.0</td>\n    </tr>\n    <tr>\n      <th>23</th>\n      <td>O2_A_08_30_L</td>\n      <td>O2</td>\n      <td>Darts ARIMAX</td>\n      <td>Min</td>\n      <td>Median</td>\n      <td>Expanding</td>\n      <td>30</td>\n      <td>Low</td>\n      <td>130</td>\n      <td>16</td>\n      <td>74</td>\n      <td>6026</td>\n      <td>0.0121</td>\n      <td>0.8904</td>\n      <td>0.1096</td>\n      <td>0.9879</td>\n      <td>0.9856</td>\n      <td>0.7429</td>\n      <td>188.0</td>\n      <td>6246.0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {}
    }
   ],
   "source": [
    "display(overview_o2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "              ID PARAMETER         MODEL RESAMPLING EXOGENOUS  TRAINSIZE  \\\n0   HR_A_01_12_H        HR         ARIMA     Median       n/a     Steady   \n1   HR_A_01_12_L        HR         ARIMA     Median       n/a     Steady   \n2   HR_A_01_30_H        HR         ARIMA     Median       n/a     Steady   \n3   HR_A_01_30_L        HR         ARIMA     Median       n/a     Steady   \n4   HR_A_02_12_H        HR         ARIMA     Median       n/a  Expanding   \n..           ...       ...           ...        ...       ...        ...   \n67  O2_A_06_30_L        O2   Darts ARIMA     Median       n/a  Expanding   \n68  O2_A_07_30_H        O2  Darts ARIMAX        Max    Median     Steady   \n69  O2_A_07_30_L        O2  Darts ARIMAX        Min    Median     Steady   \n70  O2_A_08_30_H        O2  Darts ARIMAX        Max    Median  Expanding   \n71  O2_A_08_30_L        O2  Darts ARIMAX        Min    Median  Expanding   \n\n   FIRSTFORECAST ALARMTYPE   TP   FN   FP     TN     FPR     TPR     FNR  \\\n0             12      High  131  239  157  24584  0.0231  0.3545  0.6455   \n1             12       Low   96  101  220  24694  0.0111  0.2603  0.7397   \n2             30      High   93  153   76  15444  0.0071  0.0526  0.9474   \n3             30       Low   59   67  123  15517  0.0090  0.0870  0.9130   \n4             12      High  141  229  111  24630  0.0196  0.3545  0.6455   \n..           ...       ...  ...  ...  ...    ...     ...     ...     ...   \n67            30       Low   21  100   52   6073  0.0085  0.1736  0.8264   \n68            30      High    0    0  305   5941  0.0488     NaN     NaN   \n69            30       Low  127   19   58   6042  0.0095  0.8699  0.1301   \n70            30      High    0    0  453   5793  0.0725     NaN     NaN   \n71            30       Low  130   16   74   6026  0.0121  0.8904  0.1096   \n\n       TNR     ACC     F1S  CHUNKS  ITERATIONS  \n0   0.9769  0.9485  0.3861   671.0     25111.0  \n1   0.9889  0.9668  0.3220   671.0     25111.0  \n2   0.9929  0.9621  0.0833   374.0     15766.0  \n3   0.9910  0.9552  0.1333   374.0     15766.0  \n4   0.9804  0.9518  0.4021   671.0     25111.0  \n..     ...     ...     ...     ...         ...  \n67  0.9915  0.9757  0.2165   188.0      6246.0  \n68  0.9512  0.9512  0.0000   188.0      6246.0  \n69  0.9905  0.9877  0.7674   188.0      6246.0  \n70  0.9275  0.9275  0.0000   188.0      6246.0  \n71  0.9879  0.9856  0.7429   188.0      6246.0  \n\n[72 rows x 20 columns]",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>ID</th>\n      <th>PARAMETER</th>\n      <th>MODEL</th>\n      <th>RESAMPLING</th>\n      <th>EXOGENOUS</th>\n      <th>TRAINSIZE</th>\n      <th>FIRSTFORECAST</th>\n      <th>ALARMTYPE</th>\n      <th>TP</th>\n      <th>FN</th>\n      <th>FP</th>\n      <th>TN</th>\n      <th>FPR</th>\n      <th>TPR</th>\n      <th>FNR</th>\n      <th>TNR</th>\n      <th>ACC</th>\n      <th>F1S</th>\n      <th>CHUNKS</th>\n      <th>ITERATIONS</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>HR_A_01_12_H</td>\n      <td>HR</td>\n      <td>ARIMA</td>\n      <td>Median</td>\n      <td>n/a</td>\n      <td>Steady</td>\n      <td>12</td>\n      <td>High</td>\n      <td>131</td>\n      <td>239</td>\n      <td>157</td>\n      <td>24584</td>\n      <td>0.0231</td>\n      <td>0.3545</td>\n      <td>0.6455</td>\n      <td>0.9769</td>\n      <td>0.9485</td>\n      <td>0.3861</td>\n      <td>671.0</td>\n      <td>25111.0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>HR_A_01_12_L</td>\n      <td>HR</td>\n      <td>ARIMA</td>\n      <td>Median</td>\n      <td>n/a</td>\n      <td>Steady</td>\n      <td>12</td>\n      <td>Low</td>\n      <td>96</td>\n      <td>101</td>\n      <td>220</td>\n      <td>24694</td>\n      <td>0.0111</td>\n      <td>0.2603</td>\n      <td>0.7397</td>\n      <td>0.9889</td>\n      <td>0.9668</td>\n      <td>0.3220</td>\n      <td>671.0</td>\n      <td>25111.0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>HR_A_01_30_H</td>\n      <td>HR</td>\n      <td>ARIMA</td>\n      <td>Median</td>\n      <td>n/a</td>\n      <td>Steady</td>\n      <td>30</td>\n      <td>High</td>\n      <td>93</td>\n      <td>153</td>\n      <td>76</td>\n      <td>15444</td>\n      <td>0.0071</td>\n      <td>0.0526</td>\n      <td>0.9474</td>\n      <td>0.9929</td>\n      <td>0.9621</td>\n      <td>0.0833</td>\n      <td>374.0</td>\n      <td>15766.0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>HR_A_01_30_L</td>\n      <td>HR</td>\n      <td>ARIMA</td>\n      <td>Median</td>\n      <td>n/a</td>\n      <td>Steady</td>\n      <td>30</td>\n      <td>Low</td>\n      <td>59</td>\n      <td>67</td>\n      <td>123</td>\n      <td>15517</td>\n      <td>0.0090</td>\n      <td>0.0870</td>\n      <td>0.9130</td>\n      <td>0.9910</td>\n      <td>0.9552</td>\n      <td>0.1333</td>\n      <td>374.0</td>\n      <td>15766.0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>HR_A_02_12_H</td>\n      <td>HR</td>\n      <td>ARIMA</td>\n      <td>Median</td>\n      <td>n/a</td>\n      <td>Expanding</td>\n      <td>12</td>\n      <td>High</td>\n      <td>141</td>\n      <td>229</td>\n      <td>111</td>\n      <td>24630</td>\n      <td>0.0196</td>\n      <td>0.3545</td>\n      <td>0.6455</td>\n      <td>0.9804</td>\n      <td>0.9518</td>\n      <td>0.4021</td>\n      <td>671.0</td>\n      <td>25111.0</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>67</th>\n      <td>O2_A_06_30_L</td>\n      <td>O2</td>\n      <td>Darts ARIMA</td>\n      <td>Median</td>\n      <td>n/a</td>\n      <td>Expanding</td>\n      <td>30</td>\n      <td>Low</td>\n      <td>21</td>\n      <td>100</td>\n      <td>52</td>\n      <td>6073</td>\n      <td>0.0085</td>\n      <td>0.1736</td>\n      <td>0.8264</td>\n      <td>0.9915</td>\n      <td>0.9757</td>\n      <td>0.2165</td>\n      <td>188.0</td>\n      <td>6246.0</td>\n    </tr>\n    <tr>\n      <th>68</th>\n      <td>O2_A_07_30_H</td>\n      <td>O2</td>\n      <td>Darts ARIMAX</td>\n      <td>Max</td>\n      <td>Median</td>\n      <td>Steady</td>\n      <td>30</td>\n      <td>High</td>\n      <td>0</td>\n      <td>0</td>\n      <td>305</td>\n      <td>5941</td>\n      <td>0.0488</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.9512</td>\n      <td>0.9512</td>\n      <td>0.0000</td>\n      <td>188.0</td>\n      <td>6246.0</td>\n    </tr>\n    <tr>\n      <th>69</th>\n      <td>O2_A_07_30_L</td>\n      <td>O2</td>\n      <td>Darts ARIMAX</td>\n      <td>Min</td>\n      <td>Median</td>\n      <td>Steady</td>\n      <td>30</td>\n      <td>Low</td>\n      <td>127</td>\n      <td>19</td>\n      <td>58</td>\n      <td>6042</td>\n      <td>0.0095</td>\n      <td>0.8699</td>\n      <td>0.1301</td>\n      <td>0.9905</td>\n      <td>0.9877</td>\n      <td>0.7674</td>\n      <td>188.0</td>\n      <td>6246.0</td>\n    </tr>\n    <tr>\n      <th>70</th>\n      <td>O2_A_08_30_H</td>\n      <td>O2</td>\n      <td>Darts ARIMAX</td>\n      <td>Max</td>\n      <td>Median</td>\n      <td>Expanding</td>\n      <td>30</td>\n      <td>High</td>\n      <td>0</td>\n      <td>0</td>\n      <td>453</td>\n      <td>5793</td>\n      <td>0.0725</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.9275</td>\n      <td>0.9275</td>\n      <td>0.0000</td>\n      <td>188.0</td>\n      <td>6246.0</td>\n    </tr>\n    <tr>\n      <th>71</th>\n      <td>O2_A_08_30_L</td>\n      <td>O2</td>\n      <td>Darts ARIMAX</td>\n      <td>Min</td>\n      <td>Median</td>\n      <td>Expanding</td>\n      <td>30</td>\n      <td>Low</td>\n      <td>130</td>\n      <td>16</td>\n      <td>74</td>\n      <td>6026</td>\n      <td>0.0121</td>\n      <td>0.8904</td>\n      <td>0.1096</td>\n      <td>0.9879</td>\n      <td>0.9856</td>\n      <td>0.7429</td>\n      <td>188.0</td>\n      <td>6246.0</td>\n    </tr>\n  </tbody>\n</table>\n<p>72 rows × 20 columns</p>\n</div>"
     },
     "metadata": {}
    }
   ],
   "source": [
    "# Create overview table for all parameters\n",
    "\n",
    "overview = pd.concat([\n",
    "    overview_hr,\n",
    "    overview_bp,\n",
    "    overview_o2], axis=0).reset_index(drop=True)\n",
    "\n",
    "display(overview)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import pyarrow as pa\n",
    "\n",
    "overview.to_parquet(path_to_data+'arima-x_forecast_overview_first1000.parquet', engine='pyarrow')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import pyarrow as pa\n",
    "\n",
    "overview = pd.read_parquet(path_to_data+'arima-x_forecast_overview_first1000.parquet', engine='pyarrow')"
   ]
  },
  {
   "source": [
    "## Visualization"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<Figure size 1440x936 with 12 Axes>",
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qCxWX11KXa0O+V1991Xz00Uee5xw+fNh8/PHHBS575513zIEDB4wxxvj9/pBnulqbrbqMcbc223XZXN9s1mZ73fa6thK/iXT33Xc7m+dqlu08V7Ns5w0ZMsRaljHu1mZ7jsDt49LVLNt5rmbZznN5LXW5NnizsXMiaWlp1rKMcbc2m3UZ425ttuejzfXNZm221+3TrS30p6aPMvv27XM2z9Us23muZtnO27Rpk7Usyd3abM8RuH1cupplO8/VLNt5Lq+lLtcG6bbbbrOWZSx/JpKrtdmsS3K3Ntvz0eb6ZrM22+v26dZW4jeRbJ+E0Gaeq1m281zNCkeeTa7W5mpdkczl49LVLNt5rmaFI88WV+uS3K4tUtl8smn79+tqbbY3P1ytzeWe5GpWKPJK/CYSAAAAAJwqlzfuXK3N1bokt2tDZGATCQAAAACigO1X0NhEbdHH1bokaitMid9EKl++vLN5rmbZznM1y3ae7YXY1dpszxG4fVy6mmU7z9Us23kur6Uu1wa7v9/zzjvPWpbkbm22/y51tTbb89Hm+mazNtvr9mnXdlqn5UZY7dq1yzz55JPmmWeeMbt37w5e/sILL0R1ljG/fSzh0qVLzZ49e8ygQYPM/fffb3bu3BnVWUcbPny45xm28v7zn/8YY4zJzs42I0aMMD169DCjRo0yWVlZxhhjcnJyQpr3008/mU8//dQcPHjQPPfcc+aOO+4wI0eONPv27QtpjjHGpKenF/oRnKGubcGCBebzzz83fr/fPProo2bAgAHm559/DmkGSgb6RPRlHY0+cWps9ghj6BMl3dChQ8M9BM+EsrZNmzaZ3r17mwEDBphNmzYFL7f9Cb7GhP53lp2dbcaPH29mzZpltm3bZrp3725uvvlms2HDhpDmFIeX8zE9Pd2z+zbGmAkTJhhjjNmxY4fp06ePadGihenXr58n/XbVqlVm9uzZZteuXWbgwIGmTZs25p577vFkLU1NTTU//PBDyO/3RA4dOmQmTZpkpk6davbs2WPuuOMO06VLF5ORkRGS+48xxuHXaUmaNm3aCa9LTU2N6rzbbrtNLVq0UF5ent566y298sorqlKlim6++WZNmDAharMk6f7775ck7dy5U3v27FFqaqrKli2rOXPm6J///GfUZnXp0iX4tTFGGzZsCO4ET506NaRZtvOOzIUHH3xQ1apVU4sWLfTFF1/ov//9r0aPHh3SLEnq1q2b7rnnHr333ns655xz1KxZM3355ZdatGiRXnnllZBmNWvWTOXLl1f37t3VsWNHT99r/uCDD8rv9ys7O1u7d+9W+/btVblyZU2ZMkXjxo3zLLcko09EX5ZEn4jGPJt9wmaPkOgTCJ1nnnnmhNelp6eHPO/HH3/U6NGjlZCQoN69e6tGjRqSpEceeUSPPvpoSLPS0tLUs2dP5eXladSoURo1apQuuOACpaWlaeLEiSHNOnDggGbMmKFy5crp8ssv18CBAxUbG6tHHnlEtWrVCmmWJPXq1Ut169bV9u3btXz5cg0bNkxlypTRs88+qzfeeCPkeb83YMAAT/7WlqSrr75aeXl5we/37NmjChUqSJIWLVoU8rwjfaJfv35q3ry5WrRooSVLlmj69Okh77epqakaNmyYxo4dq6uvvlrNmjXT8uXL9eabb4Z8Pv7tb39TuXLl1KhRI91yyy1KSkoK6f0frXfv3qpdu7ays7P1+eef64EHHtBZZ52lJ598MiS1+UIwxoi2ceNGLViwQO3bt3cuLycnJ/iEo27durr77rs1ceJET17CaDNLkjZv3qy33npLOTk5ateunTp16iSp8Cdf0ZB100036e2339aDDz6o0qVLe7rohyNPyn88n3jiCUlS7dq19dFHH3mSU6pUKTVs2FD//Oc/9dhjj0nKn5vvv/9+yLOqVKmil156Sc8//7zat2+vtm3bqmnTpqpWrVrIm8CPP/6oyZMnyxijNm3a6KabbpIkvfnmmyHNwW/oE9GXJdEnojVPstMnbPYIiT7hOpsbOxUrVtSUKVN01113WXlb0sMPPxzc2OnVq1dwY2fjxo2e5DVu3FiSlJKSoj59+ui1117zZNP1vvvuU926dbVu3Tq9/PLLwU2dxx57zJNNnb1796p3794KBAJq166drrjiCklSIBAIedbxNnaWLVsmKfQbOyNHjtT48eM1dOhQnX322Z5s+B3Prl271K5dO0n5m/Tjx48PeUZcXJzOP/987d+/Xx06dJAk/fWvf9Vrr70W8qyzzjpLr7/+uiZOnKgbb7xRl112mZo2baqqVavq//7v/0Ket3fvXvXv31+S1LZtW1111VUhvX/nN5Huv/9+bdy4UU2bNlW9evWcyjt8+LDWrl2r888/Xw0aNFDPnj1111136cCBA1GddcSKFSt0ySWXBBf6zZs3KycnJ6qz2rVrp9q1a2vUqFEaPHiwEhISVKVKlZDnhCPvxx9/1Pjx4+Xz+fT999/rggsu0Lfffqvc3FxP8pKTk/XBBx/oqquu0jvvvKNrrrlGn332mUqXLh3yrJiYGJUrV04PPfSQdu/erQ8++EAvv/yyfvzxR7377rshzcrLy9Pnn3+uzMxM7dq1Sxs2bFBSUlKBPxYQWvSJ6Ms6gj4RXXk2+4TNHiHRJ1xnc2OnR48eWr16tc4++2xdeeWVnmYdYWtjx+fz6ZNPPtFVV12lWrVqFdjACjWbmzpSfm1z5sxR+/btNXv2bEnSsmXLPMmzubFz2WWXKSUlRUOGDNEtt9zi+Se/rVu3To8//rhyc3P1xRdfqGHDhvrwww89yapSpYrGjRunpk2b6sUXX1SzZs302Wef6ayzzgp5ljFGPp9P//jHP9S9e3ctWbJEX3zxhWbOnBnyV1gdMWXKFO3du1d79uzRkiVLlJSUpNjYEJ0SOyRviotwu3btMlu2bHEu7/vvvzfdu3cv8B7Rd955x1x22WVRnWWMMT/88IO5++67TSAQCF525513mq+//jqqs47IzMw0d999t2nbtq1nGbbzvv/+ezN9+nTzyCOPmFmzZpl9+/aZTp06me+//96TvF27dpnBgwebli1bmgsvvNA0atTI9O3b15P3Mffv3z/k93kiGRkZplevXuall14y7733nrniiivM3/72N/PVV19ZG0NJRJ+Irixj6BPRmGezT9jsEcbQJ0qCAQMGmMWLF1vJOnTokNm7d6+VrB49epj58+ebvLw8Y4wxX3zxhWnXrp3529/+FvKsbdu2mUGDBpnMzMzgZV988YVp3759yLP+/ve/m9mzZxtjjMnNzTXGGLN06VLTvXv3kGcZk38OnyeeeKLAZUOHDvXsnEjbt283PXv2NMuWLTNpaWmeZPye3+83Dz74oGnVqpWnOXv27DFLliwxr7zyipk/f77Jysoy/fr18+RvpgMHDpgXXnjBpKammpYtW5rU1FQzYsQIs2fPnpBnHT03vLZt2zYzfPhw8+9//9ssWbLEXH/99aZHjx5m/fr1Ibl/58+JdERmZqaysrKUnJwcfB+nK3lHZwUCgdDtMoYx63h5XrKdtWfPHmVlZelPf/qTp1m282wfa7t37w7mnXHGGZ5mhWM+JiUleV4X8tEnoi/reHleok+ELsvW42izR0j0CZf5/X75/X6VK1fO86zc3FytXbtW+/fvV7ly5fTHP/5R8fHxnmRt375dzz33nAYPHhycs0uXLtWTTz4ZfEVNKNmqbefOnXr11Vf1wAMPBC979NFHlZaW5sk5kSS7vzcp/23kw4YN04oVKzx7q65UsK64uDjVq1fP07psPo65ublas2aNsrKyrGTZnB9e5jm/ibRq1SoNGzZMgUBAZcqUUXZ2towxGjJkiBo0aBDVeSfKeuSRR3TxxRdHbZbtvHBllS1bVllZWdbmo9d5J8qyMUfKli2r7OxsBQIBT2r79ttv9eijj4bluD5w4IACgYBnjyPoE9GYZTuPPuFtlo3H0cseIdEnSgJbTwA//fRTjR49WjVq1AjOpY0bNyo9PV1//etfQ54nuVubzSft4azNy40d23XZzLOZ9dlnn+npp59253EMyeuZIliXLl3Mtm3bClz2888/mxtvvDHq81zNsp3napbtPGqLvizkY+5GX5btPFezbOe5mmU7jz5h34IFC0zbtm1N7969zcCBA02vXr3M3/72N/Pxxx+HPCs1NdXs37+/wGX79u0zHTt2DHmWMe7WZrMuY9ytzfZ8tJnnapaNPOdPrJ2Xl6dzzz23wGXnnnuuZycFs5nnapbtPFezbOdRW/RlIR9zN/qybOe5mmU7z9Us23n0Cfv++c9/asqUKQU+aW///v3q0aNHyF9FkJubq8TExAKXJSQkePb7dbU2m3VJ7tZmez7azHM1y0ae85tIV111lXr06KFGjRopOTlZ2dnZWrRokZo2bRr1ea5m2c5zNct2HrVFXxbyMXejL8t2nqtZtvNczbKdR5+wz+YTwNTUVF1//fW65JJLlJycrKysLK1YsUJpaWkhz5Lcrc32k3ZXa7M9H23muZplI8/5cyJJ0vfff68VK1YETz7YoEEDXXjhhZKkn3/+OeQfZWszz9Us23muZtnOo7boy0I+5m70ZdnOczXLdp6rWbbz6BN2TZ8+XRMnTjzuE7JOnTqFPO/XX3/VqlWrgr/fevXqqVKlSpKkb775Rn/+859DluVqbbbrktytzeZ8tJ3napbneSF5U1wUs/GRiOHKczXLdp6rWbbzqC36spCPuRt9WbbzXM2ynedqlu08+oQ3du7caebPn29mz55t5s+fb3bu3Bm8buXKldbG4cXv19XaIqUuY9ytjbU0+rJCkef829mKYiy/EMtmnqtZtvNczbKdR23Rl4V8zN3oy7Kd52qW7TxXs2zn0Se8UalSJTVr1uy4140ePVoTJkywMg4vfr+u1hYpdUnu1sZaGn1ZociLDdE4opbtkxDazHM1y3aeq1m286gt+rKQj7kbfVm281zNsp3napbtPPqEfTafANr+/bpam+0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\n"
     },
     "metadata": {}
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "sns.set_style(\"whitegrid\")\n",
    "\n",
    "plotdata = overview.replace( ['_H', '_L'], ['', ''], regex=True)\n",
    "\n",
    "fig, axs = plt.subplots(\n",
    "    4,\n",
    "    3,\n",
    "    figsize = (20, 13),\n",
    "    dpi = 72\n",
    "    )\n",
    "fig.subplots_adjust(hspace = 0.8)\n",
    "\n",
    "fpr_ylimits = [0,max(overview.FPR)]\n",
    "tpr_ylimits = [0,max(overview.TPR)]\n",
    "fnr_ylimits = [0,max(overview.FNR)]\n",
    "tnr_ylimits = [0,max(overview.TNR)]\n",
    "\n",
    "for i, parameter in enumerate(['HR', 'BP', 'O2']):\n",
    "\n",
    "    sns.barplot(\n",
    "        ax = axs[0,i],\n",
    "        data = plotdata[plotdata.PARAMETER == parameter],\n",
    "        x='ID',\n",
    "        y='FPR',\n",
    "        hue='ALARMTYPE',\n",
    "        palette = sns.color_palette(\"colorblind\"),\n",
    "        ci=None)\n",
    "    axs[0,i].set_title(str(parameter), fontweight='bold', color= 'black', fontsize=14)\n",
    "    axs[0,i].set_ylim(fpr_ylimits)\n",
    "    axs[0,i].tick_params(axis=\"x\", rotation=90)\n",
    "\n",
    "    sns.barplot(\n",
    "        ax = axs[1,i],\n",
    "        data = plotdata[plotdata.PARAMETER == parameter],\n",
    "        x='ID',\n",
    "        y='TPR',\n",
    "        hue='ALARMTYPE',\n",
    "        palette = sns.color_palette(\"colorblind\"),\n",
    "        ci=None)\n",
    "    # axs[1,i].set_title(str(parameter), fontweight='bold', color= 'black', fontsize=14)\n",
    "    axs[1,i].set_ylim(tpr_ylimits)\n",
    "    axs[1,i].tick_params(axis=\"x\", rotation=90)\n",
    "\n",
    "    sns.barplot(\n",
    "        ax = axs[2,i],\n",
    "        data = plotdata[plotdata.PARAMETER == parameter],\n",
    "        x='ID',\n",
    "        y='FNR',\n",
    "        hue='ALARMTYPE',\n",
    "        palette = sns.color_palette(\"colorblind\"),\n",
    "        ci=None)\n",
    "    # axs[1,i].set_title(str(parameter), fontweight='bold', color= 'black', fontsize=14)\n",
    "    axs[2,i].set_ylim(fnr_ylimits)\n",
    "    axs[2,i].tick_params(axis=\"x\", rotation=90)\n",
    "\n",
    "    sns.barplot(\n",
    "        ax = axs[3,i],\n",
    "        data = plotdata[plotdata.PARAMETER == parameter],\n",
    "        x='ID',\n",
    "        y='TNR',\n",
    "        hue='ALARMTYPE',\n",
    "        palette = sns.color_palette(\"colorblind\"),\n",
    "        ci=None)\n",
    "    # axs[1,i].set_title(str(parameter), fontweight='bold', color= 'black', fontsize=14)\n",
    "    axs[3,i].set_ylim(tnr_ylimits)\n",
    "    axs[3,i].tick_params(axis=\"x\", rotation=90)\n",
    "\n",
    "plt.show(fig)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<Figure size 1584x504 with 6 Axes>",
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\n"
     },
     "metadata": {}
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "sns.set_style(\"whitegrid\")\n",
    "\n",
    "plotdata = overview.replace( ['_H', '_L'], ['', ''], regex=True)\n",
    "fig, axs = plt.subplots(\n",
    "    2,\n",
    "    3,\n",
    "    figsize = (20, 7),\n",
    "    dpi = 72\n",
    "    )\n",
    "fig.subplots_adjust(hspace = 0.8)\n",
    "\n",
    "acc_ylimits = [0,max(overview.ACC)]\n",
    "f1s_ylimits = [0,max(overview.F1S)]\n",
    "\n",
    "for i, parameter in enumerate(['HR', 'BP', 'O2']):\n",
    "\n",
    "    sns.barplot(\n",
    "        ax = axs[0,i],\n",
    "        data = plotdata[plotdata.PARAMETER == parameter],\n",
    "        x='ID',\n",
    "        y='ACC',\n",
    "        hue='ALARMTYPE',\n",
    "        palette = sns.color_palette(\"colorblind\"))\n",
    "    axs[0,i].set_title(str(parameter), fontweight='bold', color= 'black', fontsize=14)\n",
    "    axs[0,i].set_ylim(acc_ylimits)\n",
    "    axs[0,i].tick_params(axis=\"x\", rotation=90)\n",
    "\n",
    "    sns.barplot(\n",
    "        ax = axs[1,i],\n",
    "        data = plotdata[plotdata.PARAMETER == parameter],\n",
    "        x='ID',\n",
    "        y='F1S',\n",
    "        hue='ALARMTYPE',\n",
    "        palette = sns.color_palette(\"colorblind\"))\n",
    "    # axs[1,i].set_title(str(parameter), fontweight='bold', color= 'black', fontsize=14)\n",
    "    axs[1,i].set_ylim(f1s_ylimits)\n",
    "    axs[1,i].tick_params(axis=\"x\", rotation=90)\n",
    "\n",
    "plt.show(fig)"
   ]
  },
  {
   "source": [
    "## Comparison for selected Chunk"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "selected_chunkid = '200347.0_220045.0_2116-06-05 15:02:00'\n",
    "selected_iteration = 11"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "                                   CHUNKID ALARMTYPE  TP  FN  FP   TN\n",
       "280  200347.0_220045.0_2116-06-05 15:02:00      High   5   4   1  364\n",
       "281  200347.0_220045.0_2116-06-05 15:02:00       Low   0   0   0  374"
      ],
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>CHUNKID</th>\n      <th>ALARMTYPE</th>\n      <th>TP</th>\n      <th>FN</th>\n      <th>FP</th>\n      <th>TN</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>280</th>\n      <td>200347.0_220045.0_2116-06-05 15:02:00</td>\n      <td>High</td>\n      <td>5</td>\n      <td>4</td>\n      <td>1</td>\n      <td>364</td>\n    </tr>\n    <tr>\n      <th>281</th>\n      <td>200347.0_220045.0_2116-06-05 15:02:00</td>\n      <td>Low</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>374</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 43
    }
   ],
   "source": [
    "# 01 - ARIMA - Steady Train Size 12\n",
    "# Confusion matrix for selected chunk\n",
    "confusion_matrix_per_chunk_01_arima_steady_12_hr[\n",
    "    confusion_matrix_per_chunk_01_arima_steady_12_hr.CHUNKID == selected_chunkid]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "                                 CHUNKID  ITERATION ALARMTYPE  TP  FN  FP  TN\n",
       "0  200347.0_220045.0_2116-06-05 15:02:00         11      High   0   0   0   1\n",
       "1  200347.0_220045.0_2116-06-05 15:02:00         11       Low   0   0   0   1"
      ],
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>CHUNKID</th>\n      <th>ITERATION</th>\n      <th>ALARMTYPE</th>\n      <th>TP</th>\n      <th>FN</th>\n      <th>FP</th>\n      <th>TN</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>200347.0_220045.0_2116-06-05 15:02:00</td>\n      <td>11</td>\n      <td>High</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>200347.0_220045.0_2116-06-05 15:02:00</td>\n      <td>11</td>\n      <td>Low</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 44
    }
   ],
   "source": [
    "# 01 - ARIMA - Steady Train Size 12\n",
    "# Confusion matrix for selected iteration for selected chunk\n",
    "confusion_matrices_per_chunk_01_arima_steady_12_hr[\n",
    "    (confusion_matrices_per_chunk_01_arima_steady_12_hr.CHUNKID == selected_chunkid) &\n",
    "    (confusion_matrices_per_chunk_01_arima_steady_12_hr.ITERATION == selected_iteration)\n",
    "    ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "                                   CHUNKID ALARMTYPE  TP  FN  FP   TN\n",
       "280  200347.0_220045.0_2116-06-05 15:02:00      High   7   2   0  365\n",
       "281  200347.0_220045.0_2116-06-05 15:02:00       Low   0   0   0  374"
      ],
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>CHUNKID</th>\n      <th>ALARMTYPE</th>\n      <th>TP</th>\n      <th>FN</th>\n      <th>FP</th>\n      <th>TN</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>280</th>\n      <td>200347.0_220045.0_2116-06-05 15:02:00</td>\n      <td>High</td>\n      <td>7</td>\n      <td>2</td>\n      <td>0</td>\n      <td>365</td>\n    </tr>\n    <tr>\n      <th>281</th>\n      <td>200347.0_220045.0_2116-06-05 15:02:00</td>\n      <td>Low</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>374</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 45
    }
   ],
   "source": [
    "# 02 - ARIMA - Expanding TRAIN Size\n",
    "# Confusion matrix for selected chunk\n",
    "confusion_matrix_per_chunk_02_arima_expanding_12_hr[\n",
    "    confusion_matrix_per_chunk_02_arima_expanding_12_hr.CHUNKID == selected_chunkid]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "                                 CHUNKID  ITERATION ALARMTYPE  TP  FN  FP  TN\n",
       "0  200347.0_220045.0_2116-06-05 15:02:00         11      High   0   0   0   1\n",
       "1  200347.0_220045.0_2116-06-05 15:02:00         11       Low   0   0   0   1"
      ],
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>CHUNKID</th>\n      <th>ITERATION</th>\n      <th>ALARMTYPE</th>\n      <th>TP</th>\n      <th>FN</th>\n      <th>FP</th>\n      <th>TN</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>200347.0_220045.0_2116-06-05 15:02:00</td>\n      <td>11</td>\n      <td>High</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>200347.0_220045.0_2116-06-05 15:02:00</td>\n      <td>11</td>\n      <td>Low</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 46
    }
   ],
   "source": [
    "# 02 - ARIMA - Expanding TRAIN Size\n",
    "# Confusion matrix for selected iteration for selected chunk\n",
    "confusion_matrices_per_chunk_02_arima_expanding_12_hr[\n",
    "    (confusion_matrices_per_chunk_02_arima_expanding_12_hr.CHUNKID == selected_chunkid) &\n",
    "    (confusion_matrices_per_chunk_02_arima_expanding_12_hr.ITERATION == selected_iteration)\n",
    "    ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ]
}