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arXiv · 2405.00946

SparseTSF: Modeling Long-term Time Series Forecasting with 1k Parameters

Shengsheng Lin·Weiwei Lin·Wentai Wu·Haojun Chen·Junjie Yang
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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.

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Shengsheng Lin, Weiwei Lin, Wentai Wu, Haojun Chen, Junjie Yang. 2024-05-02. SparseTSF: Modeling Long-term Time Series Forecasting with 1k Parameters. https://arxiv.org/abs/2405.00946

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