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[FIX] Change html table to md format #596

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Jan 22, 2025
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[FIX] Change html table to md format #596

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fa5b0f1
change html table into md format
Yibei990826 Jan 21, 2025
52683ed
Add markdown table
Yibei990826 Jan 21, 2025
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95 changes: 12 additions & 83 deletions nbs/docs/getting-started/7_why_timegpt.ipynb
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Expand Up @@ -776,89 +776,18 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"<table>\n",
" <thead>\n",
" <tr>\n",
" <th>Scenario</th>\n",
" <th>TimeGPT</th>\n",
" <th>Classical Models (e.g., ARIMA)</th>\n",
" <th>Machine Learning Models (e.g., XGB, LGBM)</th>\n",
" <th>Deep Learning Models (e.g., N-HITS)</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <td>Seasonal Patterns</td>\n",
" <td>✅ Performs well with minimal setup</td>\n",
" <td>✅ Handles seasonality with adjustments (e.g., SARIMA)</td>\n",
" <td>✅ Performs well with feature engineering</td>\n",
" <td>✅ Captures seasonal patterns effectively</td>\n",
" </tr>\n",
" <tr>\n",
" <td>Non-Linear Patterns</td>\n",
" <td>✅ Excels, especially with complex non-linear patterns</td>\n",
" <td>❌ Limited performance</td>\n",
" <td>❌ Struggles without extensive feature engineering</td>\n",
" <td>✅ Performs well with non-linear relationships</td>\n",
" </tr>\n",
" <tr>\n",
" <td>Large Dataset</td>\n",
" <td>✅ Highly scalable across many series</td>\n",
" <td>❌ Slow and resource-intensive</td>\n",
" <td>✅ Scalable with optimized implementations</td>\n",
" <td>❌ Requires significant resources for large datasets</td>\n",
" </tr>\n",
" <tr>\n",
" <td>Small Dataset</td>\n",
" <td>✅ Performs well; requires only one data point to start</td>\n",
" <td>✅ Performs well; may struggle with very sparse data</td>\n",
" <td>✅ Performs adequately if enough features are extracted</td>\n",
" <td>❌ May need a minimum data size to learn effectively</td>\n",
" </tr>\n",
" <tr>\n",
" <td>Preprocessing Required</td>\n",
" <td>✅ Minimal preprocessing needed</td>\n",
" <td>❌ Requires scaling, log-transform, etc., to meet model assumptions.</td>\n",
" <td>❌ Requires extensive feature engineering for complex patterns</td>\n",
" <td>❌ Needs data normalization and preprocessing</td>\n",
" </tr>\n",
" <tr>\n",
" <td>Accuracy Requirement</td>\n",
" <td>✅ Achieves high accuracy with minimal tuning</td>\n",
" <td>❌ May struggle with complex accuracy requirements</td>\n",
" <td>✅ Can achieve good accuracy with tuning</td>\n",
" <td>✅ High accuracy possible but with significant resource use</td>\n",
" </tr>\n",
" <tr>\n",
" <td>Scalability</td>\n",
" <td>✅ Highly scalable with minimal task-specific configuration</td>\n",
" <td>❌ Not easily scalable</td>\n",
" <td>✅ Moderate scalability, with feature engineering and tuning per task</td>\n",
" <td>❌ Limited scalability due to resource demands</td>\n",
" </tr>\n",
" <tr>\n",
" <td>Computational Resources</td>\n",
" <td>✅ Highly efficient, operates seamlessly on CPU, no GPU needed</td>\n",
" <td>✅ Light to moderate, scales poorly with large datasets</td>\n",
" <td>❌ Moderate, depends on feature complexity</td>\n",
" <td>❌ High resource consumption, often requires GPU</td>\n",
" </tr>\n",
" <tr>\n",
" <td>Memory Requirement</td>\n",
" <td>✅ Efficient memory usage for large datasets</td>\n",
" <td>✅ Moderate memory requirements</td>\n",
" <td>❌ High memory usage for larger datasets or many series cases</td>\n",
" <td>❌ High memory consumption for larger datasets and multiple series</td>\n",
" </tr>\n",
" <tr>\n",
" <td>Technical Requirements & Domain Knowledge</td>\n",
" <td>✅ Low; minimal technical setup and no domain expertise needed</td>\n",
" <td>✅ Low to moderate; needs understanding of stationarity</td>\n",
" <td>❌ Moderate to high; requires feature engineering and tuning</td>\n",
" <td>❌ High; complex architecture and tuning</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n"
"| Scenario | TimeGPT | Classical Models (e.g., ARIMA) | Machine Learning Models (e.g., XGB, LGBM) | Deep Learning Models (e.g., N-HITS) |\n",
"|-----------------------------------|-------------------------------------------|-------------------------------------------------------|---------------------------------------------------------|-------------------------------------------------------|\n",
"| **Seasonal Patterns** | ✅ Performs well with minimal setup | ✅ Handles seasonality with adjustments (e.g., SARIMA) | ✅ Performs well with feature engineering | ✅ Captures seasonal patterns effectively |\n",
"| **Non-Linear Patterns** | ✅ Excels, especially with complex non-linear patterns | ❌ Limited performance | ❌ Struggles without extensive feature engineering | ✅ Performs well with non-linear relationships |\n",
"| **Large Dataset** | ✅ Highly scalable across many series | ❌ Slow and resource-intensive | ✅ Scalable with optimized implementations | ❌ Requires significant resources for large datasets |\n",
"| **Small Dataset** | ✅ Performs well; requires only one data point to start | ✅ Performs well; may struggle with very sparse data | ✅ Performs adequately if enough features are extracted | ❌ May need a minimum data size to learn effectively |\n",
"| **Preprocessing Required** | ✅ Minimal preprocessing needed | ❌ Requires scaling, log-transform, etc., to meet model assumptions | ❌ Requires extensive feature engineering for complex patterns | ❌ Needs data normalization and preprocessing |\n",
"| **Accuracy Requirement** | ✅ Achieves high accuracy with minimal tuning | ❌ May struggle with complex accuracy requirements | ✅ Can achieve good accuracy with tuning | ✅ High accuracy possible but with significant resource use |\n",
"| **Scalability** | ✅ Highly scalable with minimal task-specific configuration | ❌ Not easily scalable | ✅ Moderate scalability, with feature engineering and tuning per task | ❌ Limited scalability due to resource demands |\n",
"| **Computational Resources** | ✅ Highly efficient, operates seamlessly on CPU, no GPU needed | ✅ Light to moderate, scales poorly with large datasets | ❌ Moderate, depends on feature complexity | ❌ High resource consumption, often requires GPU |\n",
"| **Memory Requirement** | ✅ Efficient memory usage for large datasets | ✅ Moderate memory requirements | ❌ High memory usage for larger datasets or many series cases | ❌ High memory consumption for larger datasets and multiple series |\n",
"| **Technical Requirements & Domain Knowledge** | ✅ Low; minimal technical setup and no domain expertise needed | ✅ Low to moderate; needs understanding of stationarity | ❌ Moderate to high; requires feature engineering and tuning | ❌ High; complex architecture and tuning |\n"
]
}
],
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