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Dish-TS

Source code for the paper, "Dish-TS: A General Paradigm for Alleviating Distribution Shift in Time Series Forecasting", in AAAI 2023.

Overview

Dish-TS is a general paradigm for time series forecasting against distribution shift.

Usage

Similar to reversible instance normalization, Dish-TS is model-agnostic such that it can be coupled with any forecasting architectures.

Note that in experiments, we directly take the original data for training/evaluation to directly reflect the distribution shift in time series, and do not use preprocessing techniques (e.g., z-score normalization, min-max normalization) to process time series dataset.

Citation

If you find our work interesting, you can the paper as

@inproceedings{fan2023dish,
  title={Dish-TS: A General Paradigm for Alleviating Distribution Shift in Time Series Forecasting},
  author={Fan, Wei and Wang, Pengyang and Wang, Dongkun and Wang, Dongjie and Zhou, Yuanchun and Fu, Yanjie},
  booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
  volume={37},
  number={6},
  pages={7522--7529},
  year={2023}
}