期刊名称：IAENG International Journal of Computer Science
印刷版ISSN：1819-656X
电子版ISSN：1819-9224
出版年度：2021
卷号：48
期号：3
语种：English
出版社：IAENG - International Association of Engineers
摘要：The missing data imputation is a very significant topic which captures considerable interest, given the importance it has in many applications. This paper analyzes the use of GAIN (Generative Adversarial Imputation Networks) to address the problem of missing data in meteorological data sets. A detailed description of the numerical method is given together with a MATLAB implementation which will be available on request. Numerical tests are presented to validate the effectiveness of this method; moreover, a comparison on a real dataset is done with the commonly used ARMA method and GAIN turns out to be more accurate.
