论文标题
通过神经网络进行时间序列分类的数据增强的经验调查
An Empirical Survey of Data Augmentation for Time Series Classification with Neural Networks
论文作者
论文摘要
近来，深人造神经网络在模式识别方面取得了许多成功。这一成功的一部分可以归因于对大数据增加概括的依赖。但是，在时间序列识别领域，许多数据集通常很小。解决此问题的一种方法是通过使用数据增强。在本文中，我们调查了时间序列的数据增强技术及其在神经网络中的时间序列分类中的应用。我们提出了一种分类法，并概述了时间序列数据扩展中的四个家庭，包括基于转换的方法，模式混合，生成模型和分解方法。此外，我们对具有六种不同类型的神经网络的128个时间序列分类数据集进行了凭经验评估12个时间序列数据增强方法。通过结果，我们能够分析每种数据增强方法的特征，优势和缺点以及建议。这项调查旨在帮助选择神经网络应用的时间序列数据增加。
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.
