Phase-driven Domain Generalizable Learning for Nonstationary Time Series
Payal Mohapatra Lixu Wang Qi Zhu
Monitoring and recognizing patterns in continuous sensing data is crucial for
many practical applications. These real-world time-series data are often
nonstationary, characterized by varying statistical and spectral properties
over time. This poses a significant challenge in developing learning models
that can effectively generalize across different distributions. In this work,
based on our observation that nonstationary statistics are intrinsically linked
to the phase information, we propose a time-series learning framework, PhASER.
It consists of three novel elements: 1) phase augmentation that diversifies
non-stationarity while preserving discriminatory semantics, 2) separate feature
encoding by viewing time-varying magnitude and phase as independent modalities,
and 3) feature broadcasting by incorporating phase with a novel residual
connection for inherent regularization to enhance distribution invariant
learning. Upon extensive evaluation on 5 datasets from human activity
recognition, sleep-stage classification, and gesture recognition against 10
state-of-the-art baseline methods, we demonstrate that PhASER consistently
outperforms the best baselines by an average of 5% and up to 13% in some cases.
Moreover, PhASER's principles can be applied broadly to boost the
generalization ability of existing time series classification models.