CompRestacking: Capturing Channel Dependency in Highly Correlated Multivariate Time Series Data (Student Abstract)

Abstract

The consideration of channel correlation is crucial for improving the performance of multivariate time series forecasting. However, existing models fail to capture it in homogeneous and highly correlated channels. In this work, we introduce CompRestacking (Compression Restacking), a strikingly intuitive and effective method to address this problem. The approach consists of three main components: (1) PCC-Restacking for correlation-aware channel ordering, (2) Temporal embedding for time encoding, and (3) Aggregation compression for compact token generation. CompRestacking consistently outperforms in experiment results. The results demonstrate that CompRestacking leverages strong channel correlations for improved performance.