CMoS: Rethinking Time Series Prediction Through the Lens of Chunk-wise Spatial Correlations

• URL: http://arxiv.org/abs/2505.19090v1
• Date: Sun, 25 May 2025 11:01:53 GMT
• Title: CMoS: Rethinking Time Series Prediction Through the Lens of Chunk-wise Spatial Correlations
• Authors: Haotian Si, Changhua Pei, Jianhui Li, Dan Pei, Gaogang Xie,
• Abstract summary: We present CMoS, a super-lightweight time series forecasting model.<n>CMoS directly models the spatial correlations between different time series chunks.
• Score: 13.515201493037917
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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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