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

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arxiv 2405.00946 v2 pith:6Y4FISOU submitted 2024-05-02 cs.LG

Shengsheng Lin , Weiwei Lin , Wentai Wu , Haojun Chen , Junjie Yang This is my paper · ORCID
classification cs.LG
keywords sparsetsfforecastingmodelseriestechniquetimecomputationalcross-period
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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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Cited by 5 Pith papers

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  5. Mixture of Low Rank Adaptation with Partial Parameter Sharing for Time Series Forecasting

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