This paper introduces SparseTSF, a novel and extremely lightweight method 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 downsampling the original sequences to focus on cross-period trend prediction. This technique not only significantly reduces model complexity and the number of parameters but also serves as an implicit regularization mechanism that enhances the model's robustness, achieving an optimal balance between performance and efficiency. Based on this technique, SparseTSF uses fewer than 1,000 parameters to achieve competitive performance compared to state-of-the-art methods, with evident advantages under longer look-back windows (e.g., 720) that allow the model to better exploit inherent periodicity and trend information. 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.
Lin et al. (2025) studied this question.