Data Augmentation Policy Search for Long-Term Forecasting

In practice, DA has been shown to achieve state-of-the-art (SOTA) results in e.g., vision Data augmentation serves as a popular regularization [Krizhevsky et al., 2012] and natural language [Wei technique to combat overfitting challenges and Zou, 2019] tasks. in neural networks. While automatic augmentation Unfortunately, DA is not free from challenges. For instance, has demonstrated success in image classification Tian et al. [2020b] showed that the effectivity of augmented tasks, its application to time-series problems, samples depends on the downstream task. To this end, recent particularly in long-term forecasting, has received approaches explored automatic augmentation tools, where comparatively less attention. To address a good DA policy is searched for [Lemley et al., 2017, this gap, we introduce a time-series automatic augmentation Cubuk et al., 2019]. While automatic frameworks achieved approach named TSAA, which is both impressive results on image classification tasks [Zheng et al., efficient and easy to implement. The solution involves 2022] and other data modalities, problems with time-series tackling the associated bilevel optimization data received significantly less attention. Toward bridging problem through a two-step process: initially training this gap, we propose in this work a new automatic data a non-augmented model for a limited number augmentation method, designed for time-series forecasting of epochs, followed by an iterative split procedure.