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arXiv · 2009.12682

Decision-Aware Conditional GANs for Time Series Data

He Sun·Zhun Deng·Hui Chen·David C. Parkes
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Abstract

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.

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He Sun, Zhun Deng, Hui Chen, David C. Parkes. 2020-09-26. Decision-Aware Conditional GANs for Time Series Data. https://arxiv.org/abs/2009.12682

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