Creating Disasters: Recession Forecasting with GAN-Generated Synthetic Time Series Data
This addresses data scarcity in economic forecasting, particularly for rare events like recessions, but is incremental as it applies an existing GAN method to a new domain.
The paper tackles the problem of limited data availability for forecasting rare events like recessions by using a GAN-based method (DoppelGANger) to generate synthetic Treasury yield time series and recession indicators. It shows that training models on this synthetic data improves short-range Treasury yield forecasting and recession prediction, with specific performance gains over models trained only on real data.
A common problem when forecasting rare events, such as recessions, is limited data availability. Recent advancements in deep learning and generative adversarial networks (GANs) make it possible to produce high-fidelity synthetic data in large quantities. This paper uses a model called DoppelGANger, a GAN tailored to producing synthetic time series data, to generate synthetic Treasury yield time series and associated recession indicators. It is then shown that short-range forecasting performance for Treasury yields is improved for models trained on synthetic data relative to models trained only on real data. Finally, synthetic recession conditions are produced and used to train classification models to predict the probability of a future recession. It is shown that training models on synthetic recessions can improve a model's ability to predict future recessions over a model trained only on real data.