Low code Time Series Analysis
Low-code time series analysis refers to using low-code platforms to perform analysis and modeling of time series data. Time series data refers to data that is collected over time, such as stock prices, temperature readings, or web traffic.
Low-code platforms provide a visual interface for creating and deploying applications without the need for traditional programming skills. In the context of time series analysis, a low-code platform can provide tools for importing and manipulating time series data, as well as building predictive models based on that data.
There are several low-code Python libraries available for Time Series Analysis. Here are some of the most popular ones:
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PyCaret Time Series: This is a low-code library for time series forecasting and anomaly detection. It provides a simple and intuitive interface for working with time-series data and includes several built-in models for forecasting and anomaly detection.
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Darts: This is a Python library for user-friendly forecasting and anomaly detection on time series. It contains a variety of models, from classics such as ARIMA to deep neural networks.
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Streamlit Prophet: This is a low-code library for building interactive data visualizations, including time-series data. It provides a simple and intuitive interface for building web applications and includes several built-in components for creating charts, tables, and forms. Online Resource
These low-code libraries can significantly speed up the process of building time-series analysis applications, and make it easier for non-experts to work with time-series data.
- Top Python libraries for Time Series Analysis in 2022. Moez Ali, Medium.
- Time Series 101 - For beginners with PyCaret.
- PyCaret Time Series Forecasting Tutorial
- Darts: A New Approach Simplifying Time Series Analysis And Forecasting In Machine Learning. Alekhya Iavu, Medium.
- Low Code Time Series Analysis. Pier Paolo Ippolito, Towards Data Science.
Created: 03/27/2023 (C. Lizárraga); Last update: 03/29/2023 (C. Lizárraga)
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Carlos Lizárraga, Data Lab, Data Science Institute, University of Arizona.