Phase-driven Generalizable Representation Learning for Nonstationary Time Series Classification
Abstract: Pattern recognition is a fundamental task in continuous sensing applications, but real-world scenarios often experience distribution shifts that necessitate learning generalizable representations for such tasks. This challenge is exacerbated with time-series data, which also exhibit inherent \emph{nonstationarity}—variations in statistical and spectral properties over time. In this work, we offer a fresh perspective on learning generalizable representations for time-series classification by considering the phase information of a signal as an approximate proxy for nonstationarity and propose a phase-driven generalizable representation learning framework for time-series classification, \method{}. It consists of three key elements: 1) \emph{Hilbert transform-based augmentation}, which diversifies nonstationarity while preserving task-specific discriminatory semantics, 2) \emph{separate magnitude-phase encoding}, viewing time-varying magnitude and phase as independent modalities, and 3) \emph{phase-residual feature broadcasting}, integrating 2D phase features with a residual connection to the 1D signal representation, providing inherent regularization to improve distribution-invariant learning. Extensive evaluations on five datasets from sleep-stage classification, human activity recognition, and gesture recognition against 13 state-of-the-art baseline methods demonstrate that \method{} consistently outperforms the best baselines by an average of 5\% and up to 11\% in some cases. Additionally, the principles of \method{} can be broadly applied to enhance the generalizability of existing time-series representation learning models.
Submission Length: Regular submission (no more than 12 pages of main content)
Assigned Action Editor: ~Mykola_Pechenizkiy1
Submission Number: 3968
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