  In the recent years Generative Adversarial Networks (GANs) have demonstrated
significant progress in generating authentic looking data. In this work we
introduce our simple method to exploit the advancements in well established
image-based GANs to synthesise single channel time series data. We implement
Wasserstein GANs (WGANs) with gradient penalty due to their stability in
training to synthesise three different types of data; sinusoidal data,
photoplethysmograph (PPG) data and electrocardiograph (ECG) data. The length of
the returned time series data is limited only by the image resolution, we use
an image size of 64x64 pixels which yields 4096 data points. We present both
visual and quantitative evidence that our novel method can successfully
generate time series data using image-based GANs.
