TY - JOUR
TI - Using fuzzy time series forecasting and Gaussian mixture model to classify and predict new cases of COVID-19 in Saudi Arabia
AU - Alghamdi Safar M
AU - Ali Sharaf Obaid
AU - Aldahlan Maha A
AU - Abdalkrim Gaafar Mohamed
AU - Alhag Azhari A
JN - Thermal Science
PY - 2022
VL - 26
IS - 101
SP - 261
EP - 270
PT - Article
AB - 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.