On-the-fly Data Augmentation for Forecasting with Deep Learning

arXiv:2404.16918v1 Announce Type: new
Abstract: Deep learning approaches are increasingly used to tackle forecasting tasks. A key factor in the successful application of these methods is a large enough training sample size, which is not always available. In these scenarios, synthetic data generation techniques are usually applied to augment the dataset. Data augmentation is typically applied before fitting a model. However, these approaches create a single augmented dataset, potentially limiting their effectiveness. This work introduces OnDAT (On-the-fly Data Augmentation for …

abstract application arxiv augment augmentation cs.lg data data generation dataset deep learning fly forecasting key sample synthetic synthetic data tasks training type