TS-ACL: Closed-Form Solution for Time Series-oriented Continual Learning

• URL: http://arxiv.org/abs/2410.15954v3
• Date: Wed, 16 Apr 2025 12:39:13 GMT
• Title: TS-ACL: Closed-Form Solution for Time Series-oriented Continual Learning
• Authors: Jiaxu Li, Kejia Fan, Songning Lai, Linpu Lv, Jinfeng Xu, Jianheng Tang, Anfeng Liu, Houbing Herbert Song, Yutao Yue, Yunhuai Liu, Huiping Zhuang,
• Abstract summary: Time series class-incremental learning faces two major challenges: catastrophic forgetting and intra-class variations.<n>We propose TS-ACL, which leverages a gradient-free closed-form solution to avoid the catastrophic forgetting problem.<n>It also provides privacy protection and efficiency.
• Score: 16.270548433574465
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Time series classification underpins critical applications such as healthcare diagnostics and gesture-driven interactive systems in multimedia scenarios. However, time series class-incremental learning (TSCIL) faces two major challenges: catastrophic forgetting and intra-class variations. Catastrophic forgetting occurs because gradient-based parameter update strategies inevitably erase past knowledge. And unlike images, time series data exhibits subject-specific patterns, also known as intra-class variations, which refer to differences in patterns observed within the same class. While exemplar-based methods fail to cover diverse variation with limited samples, existing exemplar-free methods lack explicit mechanisms to handle intra-class variations. To address these two challenges, we propose TS-ACL, which leverages a gradient-free closed-form solution to avoid the catastrophic forgetting problem inherent in gradient-based optimization methods while simultaneously learning global distributions to resolve intra-class variations. Additionally, it provides privacy protection and efficiency. Extensive experiments on five benchmark datasets covering various sensor modalities and tasks demonstrate that TS-ACL achieves performance close to joint training on four datasets, outperforming existing methods and establishing a new state-of-the-art (SOTA) for TSCIL.

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