A novel Mamba-hypergraph enhanced time-frequency fusion network for multivariate time series classification

Abstract

Abstract Multivariate time series (MTS) classification is a crucial research area with broad applications in action recognition, healthcare, and system monitoring. Existing methods show good performance in capturing the temporal dependence of MTS, but they fail to capture the correlation between Different sEnsors at Different Timestamps (DEDT). Meanwhile, most methods ignore the frequency-domain information of the time variables, limiting the model’s capability. To overcome the above challenges, we propose the MH-TFFN model, a novel architecture integrating selective state spaces with adaptive hypergraph learning, which achieves spatio-temporal relationship modeling of multivariate time series through time-frequency dual-channel learning mode and three breakthroughs. First, a weight sharing Mamba (WSMamba) network replaces conventional sequential processing with state-space-guided feature extraction, operating simultaneously on raw time sequences and their frequency representations obtained through Fourier decomposition. Second, an adaptive hypergraph constructor dynamically establishes DEDT relationships through sliding-window correlation analysis, subsequently processed by our time-frequency hypergraph neural network (TFHGNN), which preserves both topological and time-frequency characteristics. Third, a contrastive learning mechanism employs time-frequency adversarial pairs enforces feature consistency across domains, using a novel InfoNCE-based contrastive loss to optimize the joint space. The results of the experiment demonstrate that our proposed model outperforms state-of-the-art methods across five MTS datasets.

Keywords

• Multivariate time series classification
• Time-frequency domain
• Mamba
• Hypergraph neural network
• Contrastive learning