Forecasting the Number of Respiratory Patients Hospitalized in ThiQar Hospital Using Grey Model

Abstract

The accuracy of forecasting time series  is quite a popular subject for researchers in the past and at present. However, the lack of ability of conventional analysis methods to forecast time series leads scientists and researchers to apply various forecasting models that have different mathematical backgrounds such as artificial neural networks, fuzzy predictors, evolutionary and genetic algorithms. In this paper, we will use different versions of Grey models such as GM(1,1), Expanded Grey model, the  Modified Grey model using Fourier ,Grey Verhulst model, and  the Discrete Grey Model. The monthly numbers of respiratory patients lying in the emergency lobby of Dhi Qar hospital were used to compare the accuracy of the models. We note that the best model among the five Grey models is the Grey model whose errors were corrected by adopting the Fourier series as it achieved the best value for the four comparison criteria.