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Time Series Analysis and Prediction of COVID-19 Pandemic Using Dynamic Harmonic Regression Models
College of Science and Mathematics, Augusta University, Augusta, USA
- 1 College of Science and Mathematics, Augusta University, Augusta, USA
Open Journal of Statistics·Volume 13 (2023)·Pages 222–232·Published 11 April 2023·DOI10.4236/ojs.2023.132012
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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 represent s 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 1 56 weeks COVID-19 dataset on national level using Dynamic Harmonic Regression model, including simulation analysis and accuracy improve men t from 2020 to 2023. Most importantly, we provide new advanced pathways which may serve as targets for developing new solutions and approaches.
KeywordsDynamic Harmonic Regression with ARIMA ErrorsCOVID-19 PandemicForecasting ModelsTime Series AnalysisWeekly Seasonality
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