Deep Switching Auto-Regressive Factorization (DSARF)

This repository is a user-friendly pytorch implementation of our AAAI'21 paper: Deep switching auto-regressive factorization. DSARF performs switching dynamical systems modeling.

This notebook includes several examples of using DSARF for short- and long-term forecasting and dynamical state estimation (click to open /run in Colab).

Also, check this notebook for DSARF documentation and model overview.

To access older version of our code see DSARF_v0.0

Dependencies:

Pytorch, Numpy, Scipy, Matplotlib

Citation

If you find our work useful in your research please consider citing our paper:

@inproceedings{farnoosh2021deep,
  title={Deep Switching Auto-Regressive Factorization: Application to Time Series Forecasting},
  author={Farnoosh, Amirreza and Azari, Bahar and Ostadabbas, Sarah},
  booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
  volume={35},
  number={8},
  pages={7394--7403},
  year={2021}
}