  Data augmentation (DA) has become a de facto solution to expand training data
size for deep learning. With the proliferation of deep models for time series
analysis, various time series DA techniques are proposed in the literature,
e.g., cropping-, warping-, flipping-, and mixup-based methods. However, these
augmentation methods mainly apply to time series classification and anomaly
detection tasks. In time series forecasting (TSF), we need to model the
fine-grained temporal relationship within time series segments to generate
accurate forecasting results given data in a look-back window. Existing DA
solutions in the time domain would break such a relationship, leading to poor
forecasting accuracy. To tackle this problem, this paper proposes simple yet
effective frequency domain augmentation techniques that ensure the semantic
consistency of augmented data-label pairs in forecasting, named FrAug. We
conduct extensive experiments on eight widely-used benchmarks with several
state-of-the-art TSF deep models. Our results show that FrAug can boost the
forecasting accuracy of TSF models in most cases. Moreover, we show that FrAug
enables models trained with 1\% of the original training data to achieve
similar performance to the ones trained on full training data, which is
particularly attractive for cold-start forecasting. Finally, we show that
applying test-time training with FrAug greatly improves forecasting accuracy
for time series with significant distribution shifts, which often occurs in
real-life TSF applications. Our code is available at
https://anonymous.4open.science/r/Fraug-more-results-1785.
