TSCMamba: Mamba Meets Multi-View Learning for Time Series Classification
Abstract: Time series classification (TSC) on multivariate time series is a critical problem. We propose a novel multi-view approach integrating frequency-domain and time-domain features to provide complementary contexts for TSC. Our method fuses continuous wavelet transform spectral features with temporal convolutional or multilayer perceptron features. We leverage the Mamba state space model for efficient and scalable sequence modeling. We also introduce a novel tango scanning scheme to better model sequence relationships. Experiments on 10 standard benchmark datasets demonstrate our approach achieves an average 6.45% accuracy improvement over state-of-the-art TSC models.
Submission Length: Regular submission (no more than 12 pages of main content)
Assigned Action Editor: ~Wei_Liu3
Submission Number: 2814
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