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- W4386450736 abstract "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." @default.
- W4386450736 created "2023-09-06" @default.
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- W4386450736 date "2023-07-14" @default.
- W4386450736 modified "2023-10-18" @default.
- W4386450736 title "Supervised and Unsupervised Coupled Generative Adversarial Networks in Multivariate Time Series" @default.
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- W4386450736 doi "https://doi.org/10.1109/ispds58840.2023.10235679" @default.
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