Generalized Time Series Classification via Component Decomposition and Alignment

Abstract

The objective of domain generalization is to develop a model that can handle the domain shift problem without access to the target domain. In this paper, we propose a new domain generalization approach called Decomposition Framework with Dynamic Component Alignment (DFDCA), which employs signal decomposition on input data and conducts domain alignment on each component, providing another perspective on domain generalization for time series classification. Specifically, we first utilize a neural decomposition module to decompose the original time series data into several components, and design loss functions to guide the network to effectively perform signal decomposition for class-wise domain alignment on the decomposed components. The denoising attention mechanism is then introduced to enhance informative components while suppressing task-irrelevant components. Our proposed approach is evaluated on four publicly available datasets based on the cross-domain setting where the training and test samples are drawn from different distributions. The results demonstrate that it outperforms other baseline methods, achieving state-of-the-art performance.