1. An empirical survey of data augmentation for time series classification with neural networks
- Author
-
Seiichi Uchida and Brian Kenji Iwana
- Subjects
FOS: Computer and information sciences ,Big Data ,Computer Science - Machine Learning ,Research Facilities ,Computer science ,Big data ,Social Sciences ,02 engineering and technology ,computer.software_genre ,Information Centers ,Field (computer science) ,Machine Learning (cs.LG) ,Mathematical and Statistical Techniques ,Statistics - Machine Learning ,Surveys and Questionnaires ,0202 electrical engineering, electronic engineering, information engineering ,Psychology ,Recurrent Neural Networks ,Numerical Analysis ,Multidisciplinary ,Artificial neural network ,Archives ,Pattern recognition (psychology) ,Physical Sciences ,Engineering and Technology ,Medicine ,020201 artificial intelligence & image processing ,Sensory Perception ,Research Article ,Computer and Information Sciences ,Neural Networks ,Permutation ,Science ,Machine Learning (stat.ML) ,Machine learning ,Research and Analysis Methods ,Time series ,Series (mathematics) ,business.industry ,Discrete Mathematics ,Cognitive Psychology ,Biology and Life Sciences ,020206 networking & telecommunications ,Interpolation ,Recurrent neural network ,Combinatorics ,Speech Signal Processing ,Signal Processing ,Survey data collection ,Time Domain Analysis ,Cognitive Science ,Perception ,Artificial intelligence ,Neural Networks, Computer ,business ,computer ,Mathematical Functions ,Mathematics ,Neuroscience - 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 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.
- Published
- 2021