SparseTSF: Modeling Long-term Time Series Forecasting with *1k* Parameters
Shengsheng Lin, Weiwei Lin, Wentai Wu, Haojun Chen, Junjie Yang
Abstract
This paper introduces SparseTSF, a novel, extremely lightweight model for Long-term Time Series Forecasting (LTSF), designed to address the challenges of modeling complex temporal dependencies over extended horizons with minimal computational resources. 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 technique involves downsampling the original sequences to focus on cross-period trend prediction, effectively extracting periodic features while minimizing the model's complexity and parameter count. Based on this technique, the SparseTSF model uses fewer than *1k* parameters to achieve competitive or superior performance compared to state-of-the-art models. Furthermore, SparseTSF showcases remarkable generalization capabilities, making it well-suited for scenarios with limited computational resources, small samples, or low-quality data. The code is publicly available at this repository: https://github.com/lss-1138/SparseTSF.
BibTeX
@inproceedings{
lin2024sparsetsf,
title={Sparse{TSF}: Modeling Long-term Time Series Forecasting with *1k* Parameters},
author={Shengsheng Lin and Weiwei Lin and Wentai Wu and Haojun Chen and Junjie Yang},
booktitle={Forty-first International Conference on Machine Learning},
year={2024},
url={https://openreview.net/forum?id=54NSHO0lFe}
}