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.

基于我们观察到的非稳态统计与相位信息的内在联系，我们提出了一种时间序列学习框架 PhASER，包括相位增强、分离特征编码、特征广播等元素，以增强学习模型对不同分布的泛化能力，通过在人类活动识别、睡眠阶段分类和手势识别等 5 个数据集上的广泛评估，我们证明 PhASER 相对于现有基线方法平均提高了 5% 的性能，在某些情况下提高了 13%，此外，PhASER 的原则可以广泛应用于提升现有时间序列分类模型的泛化能力。