DTWSSE: Data Augmentation with a Siamese Encoder for Time Series

TL;DR

This paper introduces DTWSSE, a novel data augmentation method for time series that uses a Siamese encoder and DTW distance to generate synthetic data, improving deep learning model performance especially on small or imbalanced datasets.

Contribution

The paper proposes a new DTW-based data augmentation technique with a Siamese encoder for interpolation, addressing limitations of existing methods like SMOTE on time series data.

Findings

Improved accuracy of deep models on augmented datasets.

Effective handling of imbalanced and small sample time series datasets.

Superior performance compared to traditional augmentation methods.

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.