Decision-Aware Conditional GANs for Time Series Data

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.

References in corpus (5)

• Conditional Generative Adversarial Nets
• Scheduled Sampling for Sequence Prediction with Recurrent Neural Networks
• Generalization and Equilibrium in Generative Adversarial Nets (GANs)
• Concentration inequalities for dependent Random variables via the martingale method
• Generative Adversarial Networks for Financial Trading Strategies Fine-Tuning and Combination

Cited by in corpus (1)

• Generative adversarial networks in time series: A survey and taxonomy