Domain generalization involves training machine learning models to perform robustly on unseen samples from out-of-distribution datasets. Adversarial Data Augmentation (ADA) is a commonly used approach that enhances model adaptability by incorporating synthetic samples, designed to simulate potential unseen samples. While ADA effectively addresses amplitude-related distribution shifts, it falls short in managing temporal shifts, which are essential for time series data. To address this limitation, we propose the Temporal Adversarial Data Augmentation for time teries Data (TADA), which incorporates a time warping technique specifically targeting temporal shifts. Recognizing the challenge of non-differentiability in traditional time warping, we make it differentiable by leveraging phase shifts in the frequency domain. Our evaluations across diverse domains demonstrate that TADA significantly outperforms existing ADA variants, enhancing model performance across time series datasets with varied distributions.
