Data Augmentation techniques in time series domain: a survey and taxonomy

With the latest advances in deep learning-based generative models, it has not taken long to take advantage of their remarkable performance in the area of time series. Deep neural networks used to work with time series heavily depend on the size and consistency of the datasets used in training. These...

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Bibliographic Details
Published in:	Neural computing & applications Vol. 35; no. 14; pp. 10123 - 10145
Main Authors:	Iglesias, Guillermo, Talavera, Edgar, González-Prieto, Ángel, Mozo, Alberto, Gómez-Canaval, Sandra
Format:	Journal Article
Language:	English
Published:	London Springer London 01.05.2023 Springer Nature B.V
Subjects:	Algorithms Artificial Intelligence Artificial neural networks Computational Biology/Bioinformatics Computational Science and Engineering Computer Science Data augmentation Data Mining and Knowledge Discovery Image Processing and Computer Vision Machine learning Performance evaluation Permutations Probability and Statistics in Computer Science Review State-of-the-art reviews Synthetic data Taxonomy Time series Deep learning Data augmentation Generative models Time series
ISSN:	0941-0643, 1433-3058
Online Access:	Get full text
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