Decision-Aware Conditional GANs for Time Series Data

• URL: http://arxiv.org/abs/2009.12682v3
• Date: Tue, 27 Oct 2020 20:18:47 GMT
• Title: Decision-Aware Conditional GANs for Time Series Data
• Authors: He Sun, Zhun Deng, Hui Chen, David C. Parkes
• Abstract summary: We introduce the decision-aware time-series conditional generative adversarial network ( DAT-CGAN) as a method for time-series generation. We improve sample efficiency through an overlapped block-sampling method, and provide a theoretical characterization of the generalization properties of DAT-CGAN.
• Score: 30.669678656710825
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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