用分块空间相关性建模时间序列，极简却更准。
CMoS: Rethinking Time Series Prediction Through the Lens of Chunk-wise Spatial Correlations
- 直接建模时间序列分块间的空间相关性，跳过复杂嵌入。
- 参数量仅1%时仍超越现有顶尖模型，多数据集验证。
- 权重可解释性强，适合需要理解时序结构的场景。
近期轻量级时间序列预测模型的发展表明，该任务本身具有内在简单性。本文提出CMoS，一种超轻量级时间序列预测模型。与学习形状嵌入不同，CMoS直接建模不同时间序列分块之间的空间相关性。我们引入相关性混合（Correlation Mixing）技术，以极少参数捕捉多样化空间相关性；另可选周期性注入（Periodicity Injection）技术，加速收敛。实验表明，尽管参数量仅为轻量模型DLinear的1%，CMoS在多个数据集上均优于现有最先进模型。此外，模型学习到的权重具有高度可解释性，为具体应用场景中的时序结构提供了有价值的洞察。
原文摘要 · Abstract (English)
Recent advances in lightweight time series forecasting models suggest the inherent simplicity of time series forecasting tasks. In this paper, we present CMoS, a super-lightweight time series forecasting model. Instead of learning the embedding of the shapes, CMoS directly models the spatial correlations between different time series chunks. Additionally, we introduce a Correlation Mixing technique that enables the model to capture diverse spatial correlations with minimal parameters, and an optional Periodicity Injection technique to ensure faster convergence. Despite utilizing as low as 1% of the lightweight model DLinear's parameters count, experimental results demonstrate that CMoS outperforms existing state-of-the-art models across multiple datasets. Furthermore, the learned weights of CMoS exhibit great interpretability, providing practitioners with valuable insights into temporal structures within specific application scenarios.
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