DTWSSE: Data Augmentation with a Siamese Encoder for Time Series

• URL: http://arxiv.org/abs/2108.09885v1
• Date: Mon, 23 Aug 2021 01:46:24 GMT
• Title: DTWSSE: Data Augmentation with a Siamese Encoder for Time Series
• Authors: Xinyu Yang, Xinlan Zhang, Zhenguo Zhang, Yahui Zhao, Rongyi Cui
• Abstract summary: We propose a DTW-based synthetic minority oversampling technique using siamese encoder for named DTWSSE. In order to reasonably measure the distance of the time series, DTW, which has been verified to be an effective method, is employed as the distance metric. The encoder is a Neural Network for mapping the time series data from the DTW hidden space to the Euclidean deep feature space, and the decoder is used to map the deep feature space back to the DTW hidden space.
• Score: 8.019203034348083
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Access to labeled time series data is often limited in the real world, which constrains the performance of deep learning models in the field of time series analysis. Data augmentation is an effective way to solve the problem of small sample size and imbalance in time series datasets. The two key factors of data augmentation are the distance metric and the choice of interpolation method. SMOTE does not perform well on time series data because it uses a Euclidean distance metric and interpolates directly on the object. Therefore, we propose a DTW-based synthetic minority oversampling technique using siamese encoder for interpolation named DTWSSE. In order to reasonably measure the distance of the time series, DTW, which has been verified to be an effective method forts, is employed as the distance metric. To adapt the DTW metric, we use an autoencoder trained in an unsupervised self-training manner for interpolation. The encoder is a Siamese Neural Network for mapping the time series data from the DTW hidden space to the Euclidean deep feature space, and the decoder is used to map the deep feature space back to the DTW hidden space. We validate the proposed methods on a number of different balanced or unbalanced time series datasets. Experimental results show that the proposed method can lead to better performance of the downstream deep learning model.

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