Missing data imputation of climate time series: A review

Missing data in climate time series is a significant problem because it complicates the monitoring and prediction of climatic phenomena. The primary objective of this research document is to describe the most relevant imputation methods for missing data in the climate context over the last decade. Results reveal a superior concentration of documents on the use of imputation methods for climate time series in Asia and Europe, with notable examples from Malaysia, China, and Italy. Meanwhile, Brazil and Australia were the countries with a high number of research in America and Oceania. Moreover, temperature and precipitation were the most frequently employed climate variables. Regarding the information source, the monitoring networks were the most commonly used source for extracting data in almost all the research. On the other hand, methods such as mean techniques, simple and multiple linear regression, interpolation, and Principal Component Analysis (PCA) were the conventional statistical techniques used for imputing missing data. Furthermore, artificial neural networks demonstrated the ability to identify complex patterns in the data. Finally, Generative Adversarial Networks excel over other deep learning methods in the imputation of missing climate data.