Secilia-Cxy / SOFTS

Official implement for "SOFTS: Efficient Multivariate Time Series Forecasting with Series-Core Fusion" in PyTorch.

SOFTS: Efficient Multivariate Time Series Forecasting with Series-Core Fusion

The code repository for SOFTS: Efficient Multivariate Time Series Forecasting with Series-Core Fusion in PyTorch. A scalable pure MLP model that achieves state-of-the-art performance on multivariate time series forecasting benchmarks.

Main Structure

Star Aggregate Dispatch Module (STAD)

Performance Comparison

Efficiency Comparison

Prerequisites

scikit-learn==1.2.2

numpy==1.22.4

pandas==1.2.4

torch==1.10.0+cu111

Datasets

We refer to this repository for downloading datasets.

Scripts

To reproduce the main results in Table 2, run the script files under folder scripts/long_term_forecast.

For example, to reproduce the results of SOFTS on ETTm1 dataset, run the following command:

sh scripts/long_term_forecast/ETT_script/SOFTS_ETTm1.sh

Acknowledgement

We appreciate the following github repos a lot for their valuable code base or datasets:

https://github.com/zhouhaoyi/Informer2020

https://github.com/thuml/Autoformer

https://github.com/zhouhaoyi/ETDataset

https://github.com/laiguokun/multivariate-time-series-data

https://github.com/thuml/Time-Series-Library

https://github.com/thuml/iTransformer

Reference

If you find our work useful in your research, please use the following citation:

@article{han2024softs,
  title={SOFTS: Efficient Multivariate Time Series Forecasting with Series-Core Fusion},
  author={Han, Lu and Chen, Xu-Yang and Ye, Han-Jia and Zhan, De-Chuan},
  journal={arXiv preprint arXiv:2404.14197},
  year={2024}
}