TSCMamba: Mamba Meets Multi-View Learning for Time Series Classification
Abstract: Multivariate time series classification (TSC) is critical for various applications in fields such as healthcare and finance. While various approaches for TSC have been explored, important properties of time series, such as shift equivariance and inversion invariance, are largely underexplored by existing works. To fill this gap, we propose a novel multi-view approach to capture patterns with properties like shift equivariance. Our method integrates diverse features, including spectral, temporal, local, and global features, to obtain rich, complementary contexts for TSC. We use continuous wavelet transform to capture time-frequency features that remain consistent even when the input is shifted in time. These features are fused with temporal convolutional or multilayer perceptron features to provide complex local and global contextual information. We utilize the Mamba state space model for efficient and scalable sequence modeling and capturing long-range dependencies in time series. Moreover, we introduce a new scanning scheme for Mamba, called tango scanning, to effectively model sequence relationships and leverage inversion invariance, thereby enhancing our model's generalization and robustness. Experiments on two sets of benchmark datasets (10 datasets each) demonstrate our approach's effectiveness, achieving average accuracy improvements of 4.01-6.45% and 8.77% respectively over leading TSC models such as TimesNet and TSLANet.
Submission Length: Regular submission (no more than 12 pages of main content)
Changes Since Last Submission: We use texts in green for new additions and red strikes for deletions. Also, we have thoroughly rewritten the introduction part. Since this part is almost completely rewritten, it is noted that we did not highlight the changes in this part (to avoid busy changes of text colors).
Assigned Action Editor: ~Wei_Liu3
Submission Number: 2814
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