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.

我们提出了一种新颖的多视图方法，将频域和时域特征整合起来，为时间序列分类提供互补的上下文。我们的方法将连续小波变换光谱特征与时间卷积或多层感知器特征融合在一起，并利用 Mamba 状态空间模型进行高效和可扩展的序列建模，同时引入一种新颖的扫描方案来更好地建模序列关系。在 10 个标准基准数据集上的实验证明，我们的方法相对于最先进的时间序列分类模型，平均准确率提高了 6.45%。