Time Series

Resources for Time Series Prediction in Python


Easily develop state of the art time series models to forecast univariate data series. Simply load your data and select which models you want to test. This is the largest repository of automated structural and machine learning time series models. Please get in contact if you want to contribute a model.


pip install atspy

Automated Models

  1. ARIMA - Automated ARIMA Modelling

  2. Prophet - Modeling Multiple Seasonality With Linear or Non-linear Growth

  3. HWAAS - Exponential Smoothing With Additive Trend and Additive Seasonality

  4. HWAMS - Exponential Smoothing with Additive Trend and Multiplicative Seasonality

  5. NBEATS - Neural basis expansion analysis (now fixed at 20 Epochs)

  6. Gluonts - RNN-based Model (now fixed at 20 Epochs)

  7. TATS - Seasonal and Trend no Box Cox

  8. TBAT - Trend and Box Cox

  9. TBATS1 - Trend, Seasonal (one), and Box Cox

  10. TBATP1 - TBATS1 but Seasonal Inference is Hardcoded by Periodicity

  11. TBATS2 - TBATS1 With Two Seasonal Periods

See full details at: https://github.com/firmai/atspy