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

Iwana, Brian Kenji ; Uchida, Seiichi Schwenker, Friedhelm

PloS one, 2021-07, Vol.16 (7), p.e0254841-e0254841 [Periódico revisado por pares]

United States: Public Library of Science

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Título:
An empirical survey of data augmentation for time series classification with neural networks
Autor: Iwana, Brian Kenji ; Uchida, Seiichi
Schwenker, Friedhelm
Assuntos: Analysis ; Archives & records ; Artificial neural networks ; Augmentation ; Big Data ; Biology and Life Sciences ; Cable television broadcasting industry ; 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
É parte de: PloS one, 2021-07, Vol.16 (7), p.e0254841-e0254841
Notas: ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
Competing Interests: The authors have declared that no competing interests exist.
Descrição: 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.
Editor: United States: Public Library of Science
Idioma: Inglês

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

Iwana, Brian Kenji ; Uchida, Seiichi Schwenker, Friedhelm

United States: Public Library of Science

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