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
ARIMA
- Automated ARIMA Modelling
Prophet
- Modeling Multiple Seasonality With Linear or Non-linear Growth
HWAAS
- Exponential Smoothing With Additive Trend and Additive Seasonality
HWAMS
- Exponential Smoothing with Additive Trend and Multiplicative Seasonality
NBEATS
- Neural basis expansion analysis (now fixed at 20 Epochs)
Gluonts
- RNN-based Model (now fixed at 20 Epochs)
TATS
- Seasonal and Trend no Box Cox
TBAT
- Trend and Box Cox
TBATS1
- Trend, Seasonal (one), and Box Cox
TBATP1
- TBATS1 but Seasonal Inference is Hardcoded by Periodicity
TBATS2
- TBATS1 With Two Seasonal Periods
See full details at: https://github.com/firmai/atspy