Simulating Health Time Series by Data Augmentation

Generating realistic simulated data for evaluating algorithms in healthcare remains a challenge as expert-based models overestimate performance on ML tasks, while data-driven models like GANs do not allow for ablation studies. To address this, we propose an approach that learns the properties of real time series, then augments simulated data with them. On glucose forecasting, we show that our method brings performance closer to that of real data compared to current simulation practices.