MSCGrapher: Learning Multi-Scale Dynamic Correlations for Multivariate Time Series Forecasting

Xian Yang, Zhenguo Zhang, Shihao Lu

Keywords: Multivariate Time Series Forecasting, Inter-Series and Intra-Series Correlations, Adaptive Correlation Learning, GNN

Abstract: Efficient learning intra-series and inter-series correlations is essential for multivariate time series forecasting (MTSF). However, in real-world scenarios, persistent and significant inter-series correlations are challenging to be represented in a static way and the strength of correlations varies across different time scales. In this paper, we address this challenge by modeling the complex inter-series relationships through dynamical correlations, considering the varying strengths of correlations. We propose a novel MTSF model: MSCGrapher, which leverages an adaptive correlation learning block to uncover inter-series correlations across different scales. Concretely, time series are first decomposed into different scales based on their periodicities. The graph representation of MTS is then constructed and an adaptive correlation learning method is introduced to capture the inter-series correlations across different scales. To quantify the strength of these correlations, we compute correlation scores based on the characteristics of the graph edges and classify correlations as either $\textit{Strong}$ or $\textit{Weak}$. Finally, we employ a self-attention module to capture intra-series correlations and then fuse features from all scales to obtain the final representation. Extensive experiments on 12 real-world datasets show that MSCGrapher gains significant forecasting performance, highlighting the critical role of inter-series correlations in capturing implicit patterns for MTS.

Readers: auai.org/UAI/2025/Conference, auai.org/UAI/2025/Conference/Area_Chairs, auai.org/UAI/2025/Conference/Reviewers, auai.org/UAI/2025/Conference/Submission317/Authors, auai.org/UAI/2025/Conference/Submission317/Reproducibility_Reviewers

Submission Number: 317