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

StyleTime: Style Transfer for Synthetic Time Series Generation