Quick and Easy Time Series Generation with Established Image-based GANs

TL;DR

This paper presents a simple method to generate single-channel time series data by leveraging established image-based GANs, demonstrating successful synthesis of various biomedical signals with high data fidelity.

Contribution

The authors introduce a novel approach that adapts image-based GANs for time series generation, expanding their applicability beyond images.

Findings

Successfully generated sinusoidal, PPG, and ECG data.

Generated time series match real data visually and quantitatively.

Method leverages image resolution to control data length.

Abstract

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.