Decision-Aware Conditional GANs for Time Series Data
Author(s)
Sun, He; Deng, Zhun; Chen, Hui; Parkes, David
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Show full item recordAbstract
We introduce the decision-aware time-series conditional generative adversarial network (DAT-CGAN), a method for the generation of time-series data that is designed to support decision-making. The framework adopts a multi-Wasserstein loss on decision-related quantities and an overlapped block-sampling approach for sample
efficiency. We characterize the generalization properties of DAT-CGAN and in application to a multi-period portfolio choice problem and financial time series data, we demonstrate better training stability and generative quality in regard to both raw data and decision-related quantities than strong GAN-based baselines.
Date issued
2023-11-27Department
Sloan School of ManagementPublisher
ACM|4th ACM International Conference on AI in Finance
Citation
Sun, He, Deng, Zhun, Chen, Hui and Parkes, David. 2023. "Decision-Aware Conditional GANs for Time Series Data."
Version: Final published version
ISBN
979-8-4007-0240-2