The World Wide Web thrives on intelligent services that rely on accurate time series classification, which has recently witnessed significant progress driven by advances in deep learning. However, existing studies face challenges in domain incremental learning. In this paper, we propose a lightweight and robust dual-causal disentanglement framework (DualCD) to enhance the robustness of models under domain incremental scenarios, which can be seamlessly integrated into time series classification models. Specifically, DualCD first introduces a temporal feature disentanglement module to capture class-causal features and spurious features. The causal features can offer sufficient predictive power to support the classifier in domain incremental learning settings. To accurately capture these causal features, we further design a dual-causal intervention mechanism to eliminate the influence of both intra-class and inter-class confounding features. This mechanism constructs variant samples by combining the current class's causal features with intra-class spurious features and with causal features from other classes. The causal intervention loss encourages the model to accurately predict the labels of these variant samples based solely on the causal features. Extensive experiments on multiple datasets and models demonstrate that DualCD effectively improves performance in domain incremental scenarios. We summarize our rich experiments into a comprehensive benchmark to facilitate research in domain incremental time series classification.

翻译：万维网的蓬勃发展依赖于精准的时间序列分类所提供的智能服务，该领域近期在深度学习的推动下取得了显著进展。然而，现有研究在领域增量学习方面面临挑战。本文提出了一种轻量且鲁棒的双因果解耦框架（DualCD），以增强模型在领域增量场景下的鲁棒性，该框架可以无缝集成到时间序列分类模型中。具体而言，DualCD首先引入一个时序特征解耦模块，以捕获类因果特征和伪特征。因果特征能够提供足够的预测能力，以支持分类器在领域增量学习设置下的工作。为了准确捕获这些因果特征，我们进一步设计了一种双因果干预机制，以消除类内和类间混杂特征的影响。该机制通过将当前类的因果特征与类内伪特征以及与其他类的因果特征相结合，构建出变体样本。因果干预损失鼓励模型仅基于因果特征来准确预测这些变体样本的标签。在多个数据集和模型上进行的大量实验表明，DualCD能有效提升领域增量场景下的性能。我们将丰富的实验结果总结为一个全面的基准，以促进领域增量时间序列分类的研究。