Supervised and Unsupervised Coupled Generative Adversarial Networks in Multivariate Time Series

Qinfen Wang, Yifei Li, Yong Xia

科研成果: 书/报告/会议事项章节会议稿件同行评审

摘要

Generative adversarial networks (GAN) have been successful in demonstrating the efficiency of generative time series. In this paper, we proposed a novel end-to-end supervised and unsupervised coupled generative adversarial networks (SUC-GAN) framework that consists of a gated recurrent unit (GRU) for generating multivariate time series. We test SUC-GAN on three datasets (i.e. Sines, Energy, Stocks) to investigate the efficacy and properties. The preliminary results show the potential power of supervised, unsupervised, and coupled learning for data generation.

源语言英语
主期刊名2023 4th International Conference on Information Science, Parallel and Distributed Systems, ISPDS 2023
出版商Institute of Electrical and Electronics Engineers Inc.
198-201
页数4
ISBN(电子版)9798350337181
DOI
出版状态已出版 - 2023
活动4th International Conference on Information Science, Parallel and Distributed Systems, ISPDS 2023 - Hybrid, Guangzhou, 中国
期限: 14 7月 202316 7月 2023

出版系列

姓名2023 4th International Conference on Information Science, Parallel and Distributed Systems, ISPDS 2023

会议

会议4th International Conference on Information Science, Parallel and Distributed Systems, ISPDS 2023
国家/地区中国
Hybrid, Guangzhou
时期14/07/2316/07/23

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