SparseTSF: Modeling Long-term Time Series Forecasting with 1k Parameters
<|reference_start|>SparseTSF: Modeling Long-term Time Series Forecasting with 1k Parameters: 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.<|reference_end|>
@article{lin2024sparsetsf:,
    title={SparseTSF: Modeling Long-term Time Series Forecasting with 1k Parameters},
    author={Shengsheng Lin, Weiwei Lin, Wentai Wu, Haojun Chen, Junjie Yang},
    journal={arXiv preprint arXiv:2405.00946},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.00946},
    primaryClass={cs.LG}
}