| Title |
Modeling COVID-19 cases using NB-INGARCH and ARIMA models: A case study in Iligan City, Philippines |
|
Authors |
Michael Ayala, Daisy Lou Polestico |
|
Publication date |
2024 |
|
Journal |
Procedia Computer Science |
| Volume |
234 |
| Pages |
262-269 |
| Publisher |
Elsevier |
| Abstract |
Modeling COVID-19 cases using count data approach has been scarce in the Philippine setting. This study compares the NB-INGARCH with the traditional ARIMA in modeling daily COVID-19 cases in Iligan City (August 14, 2020 – October 31, 2021). We employ the maximum likelihood estimation method and compare the models using the Akaike's information criterion (AIC). Empirical results reveal that the NB-INGARCH(7,0) outperforms ARIMA(2,1,3) in terms of forecast evaluation measures. However, the results show that rainfall and air pressure have no significant effects on the cases. We conclude that the NB-INGARCH model is a viable alternative approach to modeling count time series. |
| Index terms / Keywords |
ARIMAcount-time-seriesCOVID-19NB-INGARCHoverdispersedserially-correlated |
| DOI |
|
| URL |
|
Mindanao State
University - Iligan Institute of Technology