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

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Main Authors: Guillermo, Iglesias, Edgar, Talavera, Ángel, González-Prieto
Format: Book
Language:English
Published: Springer 2023
Subjects:
Data Augmentation techniques
Online Access:https://link.springer.com/article/10.1007/s00521-023-08459-3
https://dlib.phenikaa-uni.edu.vn/handle/PNK/8319
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spelling oai:localhost:PNK-83192023-04-26T03:05:16Z Data Augmentation techniques in time series domain: a survey and taxonomy Guillermo, Iglesias Edgar, Talavera Ángel, González-Prieto Data Augmentation techniques CC BY 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. 2023-04-26T03:05:16Z 2023-04-26T03:05:16Z 2023 Book https://link.springer.com/article/10.1007/s00521-023-08459-3 https://dlib.phenikaa-uni.edu.vn/handle/PNK/8319 en application/pdf Springer
institution Digital Phenikaa
collection Digital Phenikaa
language English
topic Data Augmentation techniques
spellingShingle Data Augmentation techniques
Guillermo, Iglesias
Edgar, Talavera
Ángel, González-Prieto
Data Augmentation techniques in time series domain: a survey and taxonomy
description CC BY
format Book
author Guillermo, Iglesias
Edgar, Talavera
Ángel, González-Prieto
author_facet Guillermo, Iglesias
Edgar, Talavera
Ángel, González-Prieto
author_sort Guillermo, Iglesias
title Data Augmentation techniques in time series domain: a survey and taxonomy
title_short Data Augmentation techniques in time series domain: a survey and taxonomy
title_full Data Augmentation techniques in time series domain: a survey and taxonomy
title_fullStr Data Augmentation techniques in time series domain: a survey and taxonomy
title_full_unstemmed Data Augmentation techniques in time series domain: a survey and taxonomy
title_sort data augmentation techniques in time series domain: a survey and taxonomy
publisher Springer
publishDate 2023
url https://link.springer.com/article/10.1007/s00521-023-08459-3
https://dlib.phenikaa-uni.edu.vn/handle/PNK/8319
_version_ 1764268033865416704
score 8.891145

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