An empirical survey of data augmentation for time series classification with neural networks

In recent times, deep artificial neural networks have achieved many successes in pattern recognition. Part of this success can be attributed to the reliance on big data to increase generalization. However, in the field of time series recognition, many datasets are often very small. One method of add...

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Vydáno v:PloS one Ročník 16; číslo 7; s. e0254841
Hlavní autoři: Iwana, Brian Kenji, Uchida, Seiichi
Médium: Journal Article
Jazyk:angličtina
Vydáno: United States Public Library of Science 15.07.2021
Public Library of Science (PLoS)
Témata:
Analysis
Archives & records
Artificial neural networks
Big Data
Biology and Life Sciences
Classification
Computer and Information Sciences
Data augmentation
Data entry
Datasets
Decomposition
Empirical analysis
Engineering and Technology
Evaluation
Information technology
Methods
Neural networks
Neural Networks, Computer
Pattern recognition
Physical Sciences
Polls & surveys
Research and Analysis Methods
Social Sciences
Surveys and Questionnaires
Taxonomy
Time series
Time-series analysis
Japan
ISSN:1932-6203, 1932-6203
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Popis
Shrnutí:In recent times, deep artificial neural networks have achieved many successes in pattern recognition. Part of this success can be attributed to the reliance on big data to increase generalization. However, in the field of time series recognition, many datasets are often very small. One method of addressing this problem is through the use of data augmentation. In this paper, we survey data augmentation techniques for time series and their application to time series classification with neural networks. We propose a taxonomy and outline the four families in time series data augmentation, including transformation-based methods, pattern mixing, generative models, and decomposition methods. Furthermore, we empirically evaluate 12 time series data augmentation methods on 128 time series classification datasets with six different types of neural networks. Through the results, we are able to analyze the characteristics, advantages and disadvantages, and recommendations of each data augmentation method. This survey aims to help in the selection of time series data augmentation for neural network applications.
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Competing Interests: The authors have declared that no competing interests exist.
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0254841