Decision-Aware Conditional GANs for Time Series Data
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-samplin...
| Päätekijät: | , , , |
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| Muut tekijät: | |
| Aineistotyyppi: | Artikkeli |
| Kieli: | English |
| Julkaistu: |
ACM|4th ACM International Conference on AI in Finance
2023
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| Linkit: | https://hdl.handle.net/1721.1/153133 |