Papers › SparseTSF: Modeling Long-term Time Series Forecasting with 1k Parameters
SparseTSF: Modeling Long-term Time Series Forecasting with 1k Parameters
Shengsheng Lin, Weiwei Lin, Wentai Wu, Haojun Chen, Junjie Yang
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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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Time Series Forecasting | ETTh1 (336) Multivariate | SparseTSF | MSE | 0.434 | #33 of 72 | Archive leaderboard | report |
| Time Series Forecasting | ETTh1 (720) Multivariate | SparseTSF | MSE | 0.426 | #2 of 22 | Archive leaderboard | report |
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