TITLE:
Time Series Analysis and Prediction of COVID-19 Pandemic Using Dynamic Harmonic Regression Models
AUTHORS:
Lei Wang
KEYWORDS:
Dynamic Harmonic Regression with ARIMA Errors, COVID-19 Pandemic, Forecasting Models, Time Series Analysis, Weekly Seasonality
JOURNAL NAME:
Open Journal of Statistics,
Vol.13 No.2,
April
27,
2023
ABSTRACT: 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 represents an
urgent challenge in the United States for which there are few solutions. In
this paper, we demonstrate combining Fourier
terms for capturing seasonality with ARIMA errors and other dynamics in
the data. Therefore, we have analyzed 156
weeks COVID-19 dataset on national level using Dynamic Harmonic Regression
model, including simulation analysis and accuracy improvement from 2020 to 2023. Most importantly, we
provide new advanced pathways which may serve as targets for developing new
solutions and approaches.