  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.
