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+20 datasets) demonstrate our approach's effectiveness, achieving average accuracy improvements of 4.01-6.45\% and 7.93\% respectively, over leading TSC models such as TimesNet and TSLANet.

翻译：多元时间序列分类（TSC）在医疗、金融等多个领域具有关键应用价值。尽管已有多种TSC方法被提出，但现有研究大多未充分挖掘时间序列的重要特性，如平移等变性与逆序不变性。为填补这一空白，我们提出一种新颖的多视角学习方法，以捕捉具有平移等变性等特性的模式。该方法融合频谱、时序、局部与全局特征，为TSC提供丰富且互补的上下文信息。我们采用连续小波变换获取时频特征，这些特征在输入序列发生时间平移时仍保持一致性。这些特征与时间卷积或多层感知机提取的特征相融合，共同提供复杂的局部与全局上下文信息。我们利用Mamba状态空间模型实现高效可扩展的序列建模，以捕捉时间序列中的长程依赖关系。此外，我们为Mamba设计了一种称为探戈扫描的新型扫描机制，能有效建模序列关系并利用逆序不变性，从而提升模型的泛化能力与鲁棒性。在两组基准数据集（10+20个数据集）上的实验验证了本方法的有效性，相较于TimesNet、TSLANet等主流TSC模型，平均准确率分别提升4.01-6.45%与7.93%。