TIME SERIES FORECASTING OF COVID-19 CONFIRMED CASES IN TURKEY WITH STACKING ENSEMBLE MODELS

Since COVID-19 has spread almost across any country and is a serious threat to mankind, it was declared to be a pandemic by WHO. Forecasting the results of a pandemic is a quite important and difficult task for policy makers and decision makers. The aim of this study is to forecast the daily case numbers in Turkey by using various time series modeling approaches. In this context, positive case numbers between March 11, 2020, and December 24, 2021, were taken into account in this study. This study, with the number of observations it covers, differentiates from other studies which have been conducted with few number of observations. In this study, all the waves during the COVID 19 pandemic were included in the analysis by studying a more extensive time period. Moreover, in our study, along with a comparison of machine learning algorithms by making case forecasting with these algorithms, increasing the forecasting performance was aimed by combining the predictions of all models used with the stacking approach under a single model. By taking all the related studies analyzed into account, our study, as far as we know, is the first one to assess this many model performances together and make a stacking model on COVID-19 case numbers. The findings obtained from the study prove that forecasting of the cases validated via the developed stacking model were made with high accuracy, and all ensemble learning approaches produce better results than individual methods.