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%。

TSCMamba：Mamba 与多视图学习相结合的时间序列分类

TSCMamba: Mamba Meets Multi-View Learning for Time Series Classification

We investigate the mechanisms of factual recall in the Mamba state space
model. Our work is inspired by previous findings in autoregressive transformer
language models suggesting that their knowledge recall is localized to
particular modules at specific token locations; we therefore ask whether
factual recall in Mamba can be similarly localized. To investigate this, we
conduct four lines of experiments on Mamba. First, we apply causal tracing or
interchange interventions to localize key components inside Mamba that are
responsible for recalling facts, revealing that specific components within
middle layers show strong causal effects at the last token of the subject,
while the causal effect of intervening on later layers is most pronounced at
the last token of the prompt, matching previous findings on autoregressive
transformers. Second, we show that rank-one model editing methods can
successfully insert facts at specific locations, again resembling findings on
transformer models. Third, we examine the linearity of Mamba's representations
of factual relations. Finally we adapt attention-knockout techniques to Mamba
to dissect information flow during factual recall. We compare Mamba directly to
a similar-sized transformer and conclude that despite significant differences
in architectural approach, when it comes to factual recall, the two
architectures share many similarities.

通过四条实验研究了 Mamba 状态空间模型中的实际记忆机制，发现其与自回归变压器语言模型在知识记忆的局部性和特定模块的相关性方面具有相似性，并且通过多种技术验证了 Mamba 的实际关系表示的线性性和信息流动。