Causal and Local Correlations Based Network for Multivariate Time Series Classification
This work addresses a domain-specific problem in time series analysis for researchers, but it is incremental as it builds on existing methods to incorporate correlations.
The authors tackled the problem of multivariate time series classification by addressing ignored spatial and local correlations, proposing CaLoNet which integrates causal modeling and local feature fusion, achieving competitive performance on UEA datasets.
Recently, time series classification has attracted the attention of a large number of researchers, and hundreds of methods have been proposed. However, these methods often ignore the spatial correlations among dimensions and the local correlations among features. To address this issue, the causal and local correlations based network (CaLoNet) is proposed in this study for multivariate time series classification. First, pairwise spatial correlations between dimensions are modeled using causality modeling to obtain the graph structure. Then, a relationship extraction network is used to fuse local correlations to obtain long-term dependency features. Finally, the graph structure and long-term dependency features are integrated into the graph neural network. Experiments on the UEA datasets show that CaLoNet can obtain competitive performance compared with state-of-the-art methods.