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Multivariate time series classification (MTSC) methods as a key research topic in the field of data mining, attracting extensive attention and in-depth study. However, existing multivariate time series classification methods often struggle to simultaneously capture both the complex interactions between variables within individual samples and the global relationships among different samples. To address this issue, a spatio-temporal graph attention network (STGA) classification model for multivariate time series is proposed. Firstly, a new feature extraction module comprising convolutional layers and spatio-temporal attention layers is introduced, aiming at capturing the relationships between multivariate time series variables. Secondly, a graph construction method based on Triangle Inequality and Point Clustering Dynamic Time Warping is designed to capture the relationships among samples. Based on the above techniques, a classification model based on graph convolutional modules is proposed. This model fully explores the relationships among samples and between variables within samples to obtain more accurate feature representations, thus enhancing classification accuracy. Experimental results on the UEA dataset demonstrate that this algorithm outperforms other comparative algorithms in time series classification tasks.
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