论文标题
时间序列数据的决策有条件gan
Decision-Aware Conditional GANs for Time Series Data
论文作者
论文摘要
我们介绍了决策时间序列的条件生成对抗网络（DAT-CGAN）作为时间序列生成的方法。该框架对结构化决策相关的数量采取了多陷阱损失，捕获了与决策相关数据的异质性，并为支持最终用户的决策过程提供了新的有效性。我们通过重叠的块采样方法提高了样本效率，并提供了Dat-cgan的概括特性的理论表征。该框架在财务时间序列上显示了多个时间阶段的投资组合选择问题。与强，基于GAN的基准相比，我们在基本数据和不同决策相关的量方面表现出更好的生成质量。
We introduce the decision-aware time-series conditional generative adversarial network (DAT-CGAN) as a method for time-series generation. The framework adopts a multi-Wasserstein loss on structured decision-related quantities, capturing the heterogeneity of decision-related data and providing new effectiveness in supporting the decision processes of end users. We improve sample efficiency through an overlapped block-sampling method, and provide a theoretical characterization of the generalization properties of DAT-CGAN. The framework is demonstrated on financial time series for a multi-time-step portfolio choice problem. We demonstrate better generative quality in regard to underlying data and different decision-related quantities than strong, GAN-based baselines.
