Abstract
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.
Abstract (translated)
数据增强是一种流行的正则化技术，用于解决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已经取得了成功，但其在时间序列问题上的应用，尤其是在长期预测方面，受到了相对较少的关注。为了填补这一空白，我们引入了一种名为TSAA的时间序列自动增强方法，它既高效又易于实现。解决方案涉及通过两个步骤解决相关双层优化问题：首先，对有限个 epoch 进行非增强模型训练，然后进行迭代拆分过程。在迭代过程中，我们通过贝叶斯优化识别出鲁棒增强策略，同时丢弃次优运行结果。对具有挑战性的单变量和多变量预测基准问题进行广泛的评估表明，TSAA始终优于几个稳健的基线，表明其可能被集成到预测管道中。
URL
https://arxiv.org/abs/2405.00319
