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

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.

(1)Accurate time series forecastingis crucial for decision-making in many industries, but predicting future values remains difficult. (2)We developed a lightweight time series forecasting models that analyze large volumes of sequential data to predict future outcomes with high precision. (3)This allows analysts to quickly obtain accurate forecasting results on their personal computers, thereby supporting decision-making.