Comparison of missing data imputation methods using weather data

Abstract

Researchers and data analysts commonly experience with missing data in their field of studies. It is necessary to handle missing data properly to obtain better and more reliable outcomes of any research. The objective of this research is to evaluate different imputation techniques for handling missing observations in weather data. For this purpose weather data of daily rainfall maximum temperature (Tmax) and minimum temperature (Tmin) of 23 stations of Pakistan were taken from Pakistan Metrological department from 1981 to 2020. There are total 14610 observation of each variable and each variable have different number of missing observations at different stations. For the estimation of missing observation at each station mean imputation k nearest neighbors (KNN) imputation predictive mean matching (PMM) imputation and sample imputation techniques are used. Among different imputation techniques the technique which provides minimum root mean square error (RMSE) is selected and used to estimate the missing observations at each station. The result shows that the selection of imputation method is different for each station and mainly depends upon the size of missing observation in the data. For rainfall data the KNN technique is the most appropriate while for Tmax and Tmin the mean imputation technique is recommended for the estimation of missing observation in the data as compared to other methods.