  Data augmentation serves as a popular regularization technique to combat
overfitting challenges in neural networks. While automatic augmentation has
demonstrated success in image classification tasks, its application to
time-series problems, particularly in long-term forecasting, has received
comparatively less attention. To address this gap, we introduce a time-series
automatic augmentation approach named TSAA, which is both efficient and easy to
implement. The solution involves tackling the associated bilevel optimization
problem through a two-step process: initially training a non-augmented model
for a limited number of epochs, followed by an iterative split procedure.
During this iterative process, we alternate between identifying a robust
augmentation policy through Bayesian optimization and refining the model while
discarding suboptimal runs. Extensive evaluations on challenging univariate and
multivariate forecasting benchmark problems demonstrate that TSAA consistently
outperforms several robust baselines, suggesting its potential integration into
prediction pipelines.
