Multivariate Time Series Forecasting with Multi-View Hierarchical Patching and Lag-Coupled Channel Attention

We study the problem of multivariate time-series forecasting, where existing Transformer-based models often treat time-domain dynamics, frequency-domain structure, and cross-channel relationships as separate components, limiting effective multi-view coordination and causing susceptibility to redundant information and spurious correlations. To address this, we propose HMV-Former, a forecasting architecture that integrates multi-view hierarchical patching with lag-coupled channel attention. Specifically, we construct a Hierarchical Patching Stack to build a semantic feature pyramid across multiple scales, design Lag-Coupled Channel Attention to dynamically disentangle asynchronous lagged dependencies from time-varying coupling relations, and adopt an integrated multi-view encoder with attention-logit regularization to constrain information pathways and suppress spurious dependencies in high-dimensional spaces. Experiments on 10 real-world datasets demonstrate that HMV-Former delivers substantial improvements in forecasting accuracy and stability compared to existing methods. These results highlight its advantages in multi-scale feature abstraction and dynamic dependency governance, and suggest its potential to support intelligent decision-making in complex systems. Code and datasets are publicly available at: https://github.com/FangXinbang/HMV-Former.