StiefelGen: A Simple, Model Agnostic Approach for Time Series Data Augmentation over Riemannian Manifolds

arXiv:2402.19287v1 Announce Type: new
Abstract: Data augmentation is an area of research which has seen active development in many machine learning fields, such as in image-based learning models, reinforcement learning for self driving vehicles, and general noise injection for point cloud data. However, convincing methods for general time series data augmentation still leaves much to be desired, especially since the methods developed for these models do not readily cross-over. Three common approaches for time series data augmentation include: (i) Constructing …

abstract arxiv augmentation cloud cloud data cs.lg data development driving fields general image machine machine learning noise reinforcement reinforcement learning research series simple time series type vehicles