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

ABSTRACT: 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 classific

SUBMITTER: Iwana BK 

PROVIDER: S-EPMC8282049 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature