Time Series Analysis and Prediction of COVID-19 pandemic using Dynamic Harmonic Regression Models - Wang, Lei (Journal of Model Based Research, 2023)
Rapidly spreading Covid-19 virus and its variants, especially in metropoli- tan areas around the world, became a major health public concern. The tendency of Covid-19 pandemic and statistical modelling represent an urgent challenge in the United States for which there are few solutions. In this paper, we demonstrate com- bining 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 ac- curacy improvement from 2020 to 2023. Most importantly, we provide a new advanced pathways which may serve as targets for developing new solutions and approaches.
Keywords: Time Series Analysis; COVID-19; Dynamic Harmonic Regression; ARIMA; Seasonality