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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Vydané v:Neural computing & applications Ročník 35; číslo 14; s. 10123 - 10145
Hlavní autori: Iglesias, Guillermo, Talavera, Edgar, González-Prieto, Ángel, Mozo, Alberto, Gómez-Canaval, Sandra
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: London Springer London 01.05.2023
Springer Nature B.V
Predmet:
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
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Popis
Shrnutí: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 features are not usually abundant in the real world, where they are usually limited and often have constraints that must be guaranteed. Therefore, an effective way to increase the amount of data is by using data augmentation techniques, either by adding noise or permutations and by generating new synthetic data. This work systematically reviews the current state of the art in the area to provide an overview of all available algorithms and proposes a taxonomy of the most relevant research. The efficiency of the different variants will be evaluated as a central part of the process, as well as the different metrics to evaluate the performance and the main problems concerning each model will be analysed. The ultimate aim of this study is to provide a summary of the evolution and performance of areas that produce better results to guide future researchers in this field.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
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content type line 14
ISSN:0941-0643
1433-3058
DOI:10.1007/s00521-023-08459-3