Many systems used in real-world environments require adding new categories and incorporating new information without forgetting what was previously learnt by the classification model. This is known as class-incremental continual learning, and in the case of multivariate time-series, is further complicated by the temporal structure of the data. In this paper, we present a novel approach for performing class incremental continual learning for the classification of multivariate time series data based upon the construction of a dual-stream feature extraction pipeline (using both deep temporal embedding features generated via a pre-trained frozen foundation model and application of statistical features). Evaluated on five benchmark datasets, the proposed system achieves competitive average accuracy across all datasets while maintaining low forgetting rates across all experimental configurations.

翻译：现实环境中的许多系统需要在不遗忘分类模型先前所学知识的前提下，添加新类别并整合新信息。这被称为类增量持续学习，而在多变量时间序列场景中，数据的时序结构进一步增加了其复杂性。本文提出了一种基于双流特征提取流水线（利用预训练冻结基础模型生成的深度时间嵌入特征与统计特征相结合）的多变量时间序列数据类增量持续学习方法。在五个基准数据集上的评估表明，所提系统在所有数据集上均能保持具有竞争力的平均准确率，同时在不同实验配置下均维持较低的遗忘率。