Statistical Time Series Forecasting Models for Pandemic Prediction

作     者：Ahmed ElShafee Walid El-Shafai Abeer D.Algarni Naglaa F.Soliman Moustafa H.Aly 

作者机构：Department of Electrical EngineeringFaculty of EngineeringAhram Canadian University6th October CityGizaEgypt Security Engineering LabComputer Science DepartmentPrince Sultan UniversityRiyadh11586Saudi Arabia Department of Electronics and Electrical Communications EngineeringFaculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Department of Information TechnologyCollege of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Electronics and Communications Engineering DepartmentCollege of Engineering and TechnologyArab Academy for ScienceTechnology and Maritime TransportAlexandria1029Egypt 

出 版 物：《Computer Systems Science & Engineering》 (计算机系统科学与工程（英文）)

年 卷 期：2023年第47卷第10期

页      面：349-374页

学科分类：0711[理学-系统科学] 07[理学] 08[工学] 081101[工学-控制理论与控制工程] 0811[工学-控制科学与工程] 071102[理学-系统分析与集成] 081103[工学-系统工程] 

基　　金：The authors extend their appreciation to the Deputyship for Research&Innovation Ministry of Education in Saudi Arabia for funding this research work through the project number RI-44-0525 

主　　题：Forecasting COVID-19 predictive models medical viruses mathematical model market research diseases 

摘      要：COVID-19 has significantly impacted the growth prediction of a pandemic,and it is critical in determining how to battle and track the disease *** this case,COVID-19 data is a time-series dataset that can be projected using different ***,this work aims to gauge the spread of the outbreak severity over ***,data analytics and Machine Learning(ML)techniques are employed to gain a broader understanding of virus *** have simulated,adjusted,and fitted several statistical time-series forecasting models,linearML models,and nonlinear ML *** of these models are Logistic Regression,Lasso,Ridge,ElasticNet,Huber Regressor,Lasso Lars,Passive Aggressive Regressor,K-Neighbors Regressor,Decision Tree Regressor,Extra Trees Regressor,Support Vector Regressions(SVR),AdaBoost Regressor,Random Forest Regressor,Bagging Regressor,AuoRegression,MovingAverage,Gradient Boosting Regressor,Autoregressive Moving Average(ARMA),Auto-Regressive Integrated Moving Averages(ARIMA),SimpleExpSmoothing,Exponential Smoothing,Holt-Winters,Simple Moving Average,Weighted Moving Average,Croston,and naive ***,our suggested methodology includes the development and evaluation of ensemble models built on top of the best-performing statistical and ML-based prediction methods.A third stage in the proposed system is to examine three different implementations to determine which model delivers the best ***,this best method is used for future forecasts,and consequently,we can collect the most accurate and dependable predictions.