A novel two-enhancive aspect module in convolutional neural networks for multivariate time series classification

Multivariate Time Series Classification (MTSC) presents a significant challenge in time series data mining. While many methods have been proposed, Convolutional Neural Networks (CNNs) still face limitations in effectively capturing multivariate dependencies. In this paper, we introduce the Two-Enhancive Aspect Module (TEAM), a novel attention mechanism that integrates both Temporal Attention (TEAM_T) and Channel Attention (TEAM_C) to enhance feature extraction in CNNs. In TEAM_T, the calculation of attention weights considers both the sample's content and its relative positional placement, forming a pseudo-Gaussian distribution that better reflects the relative importance of time samples. In TEAM_C, the Discrete Cosine Transform (DCT) is used for time–frequency transformation, building channel attention in the frequency domain, it better captures the interdependencies between channels. Evaluated on the UEA benchmark with 15 MTSC datasets, TEAM consistently outperforms 14 baseline methods and 5 alternative attention mechanisms, significantly boosting classification performance and surpassing the current SOTA in mean accuracy.