Hybrid intelligent parameter tuning approach for COVID-۱۹ time series modeling and prediction

A novel hybrid intelligent approach for tuning the parameters of Interval Type-۲ Intuitionistic Fuzzy Logic System (IT۲IFLS) is introduced for the modeling and prediction of coronavirus disease ۲۰۱۹ (COVID-۱۹) time series. COVID-۱۹ is known to be a virus caused by Severe Acute Respiratory Syndrome Coronavirus ۲ (SARSCoV-۲) with a huge negative impact on human, work and world economy. Globally, more than ۱۰۰ million people have been infected with over two million deaths and it is not certain when the pandemic will end. Predicting the trend of the COVID-۱۹ therefore becomes an important and challenging task. Many approaches ranging from statistical approaches to machine learning methods have been formulated and applied for the prediction of the disease. In this work, the sliding mode control learning algorithm is used to adjust the parameters of the antecedent parts of  IT۲IFLS system while the gradient descent backpropagation is adopted to tune the consequent parameters in a hybrid manner. The results of the hybrid intelligent learning model are compared with results of single learning models using sliding mode control and gradient descent algorithms and found to provide good performance in terms of Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE) especially in noisy environments. The type-۲ hybrid model also outperforms its type-۱ counterparts in the different problem instances.

Keywords:

Interval type-۲ intuitionistic fuzzy set , Gradient descent algorithm , sliding mode control algorithm , intuitionistic fuzzy index