Using fuzzy time series forecasting and Gaussian mixture model to classify and predict new cases of COVID-19 in Saudi Arabia

Abstract

In light of the global events resulting from the spread of the Corona pandemic and viral mutations, there is a need to examine epidemic data in terms of numbers of infected and deaths, different geographical locations, and the dynamics of disease dissemination virus. In the Kingdom of Saudi Arabia (KSA), since the spread of the virus on March 2, 2020, the number of confirmed cases has increased to 599044 cases until January 13, 2022, of which 262 are critical cases, while the number of recovery cases have reached 55035 cases, and deaths are 8901. It is a serious disease, and its spread is difficult to contain. The number of cases has continued to grow rapidly since the first cases appeared. Guess and Buck?s model for forecasting time-series data is an important figure that cannot be crossed when predicting fuzzy time-series, although several modifications have been made to the model to improve the accuracy of its results. The Gaussian mixture model and the fuzzy method for modelling new cases in Saudi Arabia were used as machine learning methods to classify and predict new cases of the virus in Saudi Arabia. Foggy time series forecasting. The studied datasets from the World Health Organization from May 15 to August 12, 2020 were used.