# Decision-Aware Conditional GANs for Time Series Data

arXiv: https://arxiv.org/abs/2009.12682 · HTML: https://mlsanity.com/paper/2009.12682/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Sep 2020.

## Built on by (most cited first, 11 of 11)

- [PSA-GAN: Progressive Self Attention GANs for Synthetic Time Series](https://mlsanity.com/paper/2108.00981/) (2021)
- [Generative adversarial networks in time series: A survey and taxonomy](https://mlsanity.com/paper/2107.11098/) (2021)
- [Time Series Generation with Masked Autoencoder](https://mlsanity.com/paper/2201.07006/) (2022)
- [Reinforcement Learning with Stepwise Fairness Constraints](https://mlsanity.com/paper/2211.03994/) (2022)
- [Constrained Adversarial Learning for Automated Software Testing: a literature review](https://mlsanity.com/paper/2303.07546/) (2023)
- [Time Series (re)sampling using Generative Adversarial Networks](https://mlsanity.com/paper/2102.00208/) (2021)
- [Synthetic Data for Portfolios: A Throw of the Dice Will Never Abolish Chance](https://mlsanity.com/paper/2501.03993/) (2025)
- [Regret Equals Covariance: A Closed-Form Characterization for Stochastic Optimization](https://mlsanity.com/paper/2605.14019/) (2026)
- [NeuralFactors: A Novel Factor Learning Approach to Generative Modeling of Equities](https://mlsanity.com/paper/2408.01499/) (2024)
- [Multiscale Generative Models: Improving Performance of a Generative Model Using Feedback from Other Dependent Generative Models](https://mlsanity.com/paper/2201.09644/) (2022)
- [DE-CGAN: Boosting rTMS Treatment Prediction with Diversity Enhancing Conditional Generative Adversarial Networks](https://mlsanity.com/paper/2404.16913/) (2024)

## Abstract (arXiv)

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.