Du Mingsen, Wei Yanxuan, Tang Yingxia, Zheng Xiangwei, Wei Shoushui, Ji Cun
School of Control Science and Engineering, Shandong University, Jinan, China; School of Information Science and Engineering, Shandong Normal University, Jinan, China.
School of Information Science and Engineering, Shandong Normal University, Jinan, China.
Neural Netw. 2025 Mar;183:106951. doi: 10.1016/j.neunet.2024.106951. Epub 2024 Dec 3.
Multivariate time series classification is of great importance in practical applications and is a challenging task. However, deep neural network models such as Transformers exhibit high accuracy in multivariate time series classification but lack interpretability and fail to provide insights into the decision-making process. On the other hand, traditional approaches based on decision tree classifiers offer clear decision processes but relatively lower accuracy. Swin Transformer (ST) addresses these issues by leveraging self-attention mechanisms to capture both fine-grained local patterns and global patterns. It can also model multi-scale feature representation learning, thereby providing a more comprehensive representation of time series features. To tackle the aforementioned challenges, we propose ST-Tree with interpretability for multivariate time series classification. Specifically, the ST-Tree model combines ST as the backbone network with an additional neural tree model. This integration allows us to fully leverage the advantages of ST in learning time series context while providing interpretable decision processes through the neural tree. This enables researchers to gain clear insights into the model's decision-making process and extract meaningful interpretations. Through experimental evaluations on 10 UEA datasets, we demonstrate that the ST-Tree model improves accuracy in multivariate time series classification tasks and provides interpretability through visualizing the decision-making process across different datasets.
多变量时间序列分类在实际应用中非常重要，并且是一项具有挑战性的任务。然而，诸如Transformer之类的深度神经网络模型在多变量时间序列分类中表现出高精度，但缺乏可解释性，并且无法深入了解决策过程。另一方面，基于决策树分类器的传统方法提供了清晰的决策过程，但准确性相对较低。Swin Transformer（ST）通过利用自注意力机制来捕获细粒度的局部模式和全局模式来解决这些问题。它还可以对多尺度特征表示学习进行建模，从而提供更全面的时间序列特征表示。为了解决上述挑战，我们提出了用于多变量时间序列分类的具有可解释性的ST-Tree。具体而言，ST-Tree模型将ST作为骨干网络与一个额外的神经树模型相结合。这种集成使我们能够充分利用ST在学习时间序列上下文方面的优势，同时通过神经树提供可解释的决策过程。这使研究人员能够清楚地了解模型的决策过程并提取有意义的解释。通过对10个UEA数据集的实验评估，我们证明了ST-Tree模型提高了多变量时间序列分类任务的准确性，并通过可视化不同数据集的决策过程提供了可解释性。
