  Neural style transfer is a powerful computer vision technique that can
incorporate the artistic "style" of one image to the "content" of another. The
underlying theory behind the approach relies on the assumption that the style
of an image is represented by the Gram matrix of its features, which is
typically extracted from pre-trained convolutional neural networks (e.g.,
VGG-19). This idea does not straightforwardly extend to time series stylization
since notions of style for two-dimensional images are not analogous to notions
of style for one-dimensional time series. In this work, a novel formulation of
time series style transfer is proposed for the purpose of synthetic data
generation and enhancement. We introduce the concept of stylized features for
time series, which is directly related to the time series realism properties,
and propose a novel stylization algorithm, called StyleTime, that uses explicit
feature extraction techniques to combine the underlying content (trend) of one
time series with the style (distributional properties) of another. Further, we
discuss evaluation metrics, and compare our work to existing state-of-the-art
time series generation and augmentation schemes. To validate the effectiveness
of our methods, we use stylized synthetic data as a means for data augmentation
to improve the performance of recurrent neural network models on several
forecasting tasks.
