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To enhance the accuracy and efficacy of multivariate time series similarity measurement, this paper proposes a sliding window approach based on Transformer. Specifically, each dimension of the multivariate time series is processed through sliding windows and input into a Transformer for feature extraction. By using multiple window sizes, the method simultaneously captures localized temporal segment features and identifies local patterns within the time series. Encoded window features for each sample are combined to form a comprehensive feature sequence that represents the global characteristics of the entire time series. These global features are then used to compute the final similarity measure through Dynamic Time Warping (DTW). This approach effectively captures both local and global features of multivariate time series, significantly improving similarity measurement precision. The effectiveness of the proposed method is validated through 1-Nearest Neighbor (1NN) classification experiments, demonstrating superior accuracy and enhanced performance in similarity measurement. 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