Time Series Data Augmentation as an Imbalanced Learning Problem

arXiv:2404.18537v1 Announce Type: new
Abstract: Recent state-of-the-art forecasting methods are trained on collections of time series. These methods, often referred to as global models, can capture common patterns in different time series to improve their generalization performance. However, they require large amounts of data that might not be readily available. Besides this, global models sometimes fail to capture relevant patterns unique to a particular time series. In these cases, data augmentation can be useful to increase the sample size of …

abstract art arxiv augmentation cs.lg data forecasting global however patterns performance series state stat.ml time series type