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.

翻译：我们提出决策感知时间序列条件生成对抗网络（DAT-CGAN），作为一种时间序列生成方法。该框架在结构化决策相关量上采用多Wasserstein损失，捕捉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新的有效性。我们通过重叠块采样方法提升了样本效率，并对DAT-CGAN的泛化性质进行了理论刻画。该框架在金融时间序列上，针对多时间步投资组合选择问题进行了演示。我们展示了相比基于GAN的强基线方法，在底层数据和不同决策相关量方面更优的生成质量。