[TPAMI 2025 & ICML 2024 Oral] "SparseTSF: Modeling Long-term Time Series Forecasting with 1k Parameters". Please refer to this repository for the full code and details.
SparseTSF is a novel, extremely lightweight model for Long-term Time Series Forecasting (LTSF). At the heart of SparseTSF lies the Cross-Period Sparse Forecasting technique, which simplifies the forecasting task by decoupling the periodicity and trend in time series data.
This work was developed by the Advanced Computing Architecture Team, School of Computer Science and Engineering, South China University of Technology; Pengcheng Laboratory.
The top three main contributors are:
- Shengsheng Lin ([email protected])
- Weiwei Lin ([email protected]) [Corresponding author]
- Wentai Wu ([email protected])
If you find this work useful, please cite our paper.
@article{lin2024sparsetsf,
title={SparseTSF: Modeling Long-term Time Series Forecasting with 1k Parameters},
author={Lin, Shengsheng and Lin, Weiwei and Wu, Wentai and Chen, Haojun and Yang, Junjie},
journal={arXiv preprint arXiv:2405.00946},
year={2024}
}