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.

翻译：数据增强作为一种常用的正则化技术，旨在应对神经网络中的过拟合挑战。虽然自动增强在图像分类任务中取得了成功，但其在时间序列问题中的应用，尤其是在长期预测领域，受到的关注相对较少。为弥补这一空白，我们提出了一种名为TSAA的时间序列自动增强方法，该方法既高效又易于实施。解决方案涉及通过两步过程处理相关的双层优化问题：首先使用有限数量的周期训练一个未增强模型，随后进入迭代拆分程序。在此迭代过程中，我们交替进行以下操作：通过贝叶斯优化识别鲁棒的数据增强策略，并在丢弃次优运行的同时优化模型。在具有挑战性的单变量和多变量预测基准问题上的广泛评估表明，TSAA始终优于多个稳健基线，这暗示了其有潜力被集成到预测流程中。