%0 Journal Article
%T Time Series Analysis and Prediction of COVID-19 Pandemic Using Dynamic Harmonic Regression Models
%A Lei Wang
%J Open Journal of Statistics
%P 222-232
%@ 2161-7198
%D 2023
%I Scientific Research Publishing
%R 10.4236/ojs.2023.132012
%X Rapidly
spreading COVID-19 virus and its variants, especially in metropolitan areas
around the world, became a major health public concern. The tendency of COVID-19
pandemic and statistical modelling represent</span></span><span style=\"font-family:Verdana;\"><span style=\"font-family:Verdana;\"><span style=\"font-family:Verdana;\">s</span></span></span><span><span><span style=\"font-family:&quot;\"><span style=\"font-family:Verdana;\"> an
urgent challenge in the United States for which there are few solutions. In
this paper, </span><span style=\"font-family:Verdana;\">we demonstrate combining Fourier
terms for capturing seasonality with</span><span style=\"font-family:Verdana;\"> ARIMA errors and other dynamics in
the data. Therefore, we have analyzed 1</span><span style=\"font-family:Verdana;\">56
weeks COVID-19 dataset on national level using Dynamic Harmonic </span><span style=\"font-family:Verdana;\">Regression
model, including simulation analysis and accuracy improve</span><span style=\"font-family:Verdana;\">men</span><span style=\"font-family:Verdana;\">t from 2020 to 2023. Most importantly, we
provide</span></span></span></span><span><span><span style=\"font-family:&quot;\"> </span></span></span><span style=\"font-family:Verdana;\"><span style=\"font-family:Verdana;\"><span style=\"font-family:Verdana;\">new advanced pathways which may serve as targets for developing new
solutions and approaches.
%K Dynamic Harmonic Regression with ARIMA Errors
%K COVID-19 Pandemic
%K Forecasting Models
%K Time Series Analysis
%K Weekly Seasonality
%U http://www.scirp.org/journal/PaperInformation.aspx?PaperID=124532