论文标题
使用生成的对抗网络合成人工财务数据集
Using generative adversarial networks to synthesize artificial financial datasets
论文作者
论文摘要
生成的对抗网络（GAN）在产生现实的图像中非常流行。在本文中，我们建议使用gans合成人工财务数据进行研究和基准测试。我们在三个American Express数据集上测试了这种方法，并表明经过适当训练的gan可以以高保真度复制这些数据集。在我们的实验中，我们定义了一种新型的GAN类型，并提出了数据预处理的方法，以允许良好的训练和测试GAN的性能。我们还讨论了评估生成数据质量的方法，以及它们与原始实际数据的比较。
Generative Adversarial Networks (GANs) became very popular for generation of realistically looking images. In this paper, we propose to use GANs to synthesize artificial financial data for research and benchmarking purposes. We test this approach on three American Express datasets, and show that properly trained GANs can replicate these datasets with high fidelity. For our experiments, we define a novel type of GAN, and suggest methods for data preprocessing that allow good training and testing performance of GANs. We also discuss methods for evaluating the quality of generated data, and their comparison with the original real data.
