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.

翻译：罕见事件（如经济衰退）预测中的一个常见问题是数据可用性有限。深度学习与生成对抗网络（GAN）的最新进展使得大规模生成高保真合成数据成为可能。本文采用名为DoppelGANger的模型（一种专为生成合成时间序列数据而设计的GAN），生成合成国债收益率时间序列及相关衰退指标。研究表明，相较于仅使用真实数据训练的模型，基于合成数据训练的模型在短期国债收益率预测任务中表现更优。最后，本文生成了合成衰退条件，并利用其训练分类模型以预测未来经济衰退的概率。实验证明，相较于仅使用真实数据训练的模型，基于合成衰退数据训练模型能够提升其预测未来经济衰退的能力。