Date: 25 October 2020 | Speaker: Safia Al Marhoobi
Decision making processes are mostly dependent on the availability of data from sources which can be extracted information. Availability of data without any missing values that lead to get significant results and to meet the needs of scientific research. Moreover, the incomplete record lead to biased results in statistical methods and affect the quality measurements significantly. Meteorological time series in this study contain missing data. Thus, we are faced to the problem of imputing missing values before running statistical procedures on the time series. This study illustrates a variety of advanced methods to handle missing data and filling gaps. By choosing the proper imputing technique depends on the structure of time series concerned which are Singular Spectrum Analysis by using Iterative approach and regression methods and regression with lagging. These methods candidate solutions to the obstacle of missing data imputation that can often get reasonable results for addressing missing data.
