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Retrieved on: 2024-08-01 01:58:48
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Summary
The article explores using Generative Adversarial Networks (specifically TimeGAN) for generating time series data to predict crop water productivity, leveraging Bayesian networks for dynamic modeling. Tags related to computational statistics, dimension reduction, and machine learning highlight the used methodologies.
Article found on: www.nature.com
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