  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.
