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:在预测衰退等罕见事件时,常见的问题是数据可得性有限;最近深层次学习和基因对抗网络(GANs)的进步使得能够大量生成高纤维合成数据;本文使用一个名为DoppelGANger的模型,这是一个为制作合成时间序列数据而定制的GAN 模型,以生成合成的财政部产出时间序列和相关衰退指标;然后,可以表明,国库产量的短程预测性能得到改进,用于所培训的合成数据模型与仅经过实际数据培训的模型相比。最后,合成衰退条件被制作并用于培训分类模型,以预测未来衰退的概率。文件表明,合成衰退培训模型比仅经过实际数据培训的模型更有能力预测未来衰退。