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

TL;DR

CMoS is a super-lightweight time series forecasting model that directly models spatial correlations between data chunks, outperforming larger models while offering interpretability and fast convergence.

Contribution

Introduces CMoS, a novel lightweight model that captures spatial correlations and incorporates correlation mixing and periodicity injection techniques.

Findings

Outperforms state-of-the-art models with only 1% of parameters.

Demonstrates superior accuracy across multiple datasets.

Provides interpretable weights revealing temporal structures.

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.