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
available at: this https URL

SparseTSF 是一种全新的、极其轻量级的用于长期时间序列预测（LTSF）的模型，通过最小化计算资源，解决了在扩展时间范围内对复杂时间依赖性进行建模的挑战。SparseTSF 的核心是交叉周期稀疏预测技术，通过降采样序列来专注于交叉周期趋势预测，有效提取周期特征并减小模型复杂性和参数数量。基于该技术，SparseTSF 模型使用少于 1k 个参数实现了与最先进模型相媲美或更优的性能。此外，SparseTSF 展示了显著的泛化能力，适用于计算资源有限、样本较少或质量较低的情境。