Improvement on Forecasting of Propagation of the COVID-19 Pandemic through Combining Oscillations in ARIMA Models
Daily data on COVID-19 infections and deaths tend to possess weekly oscillations. The purpose of this work is to forecast COVID-19 data with partially cyclical fluctuations. A partially periodic oscillating ARIMA model is suggested to enhance the predictive performance. The model, optimized for impr...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-12-01
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| Series: | Forecasting |
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| Online Access: | https://www.mdpi.com/2571-9394/6/1/2 |
| _version_ | 1797241017997459456 |
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| author | Eunju Hwang |
| author_facet | Eunju Hwang |
| author_sort | Eunju Hwang |
| collection | DOAJ |
| description | Daily data on COVID-19 infections and deaths tend to possess weekly oscillations. The purpose of this work is to forecast COVID-19 data with partially cyclical fluctuations. A partially periodic oscillating ARIMA model is suggested to enhance the predictive performance. The model, optimized for improved prediction, characterizes and forecasts COVID-19 time series data marked by weekly oscillations. Parameter estimation and out-of-sample forecasting are carried out with data on daily COVID-19 infections and deaths between January 2021 and October 2022 in the USA, Germany, and Brazil, in which the COVID-19 data exhibit the strongest weekly cycle behaviors. Prediction accuracy measures, such as RMSE, MAE, and HMAE, are evaluated, and 95% prediction intervals are constructed. It was found that predictions of daily COVID-19 data can be improved considerably: a maximum of 55–65% in RMSE, 58–70% in MAE, and 46–60% in HMAE, compared to the existing models. This study provides a useful predictive model for the COVID-19 pandemic, and can help institutions manage their healthcare systems with more accurate statistical information. |
| first_indexed | 2024-04-24T18:16:39Z |
| format | Article |
| id | doaj.art-32802a47aeee423287f386a529216258 |
| institution | Directory Open Access Journal |
| issn | 2571-9394 |
| language | English |
| last_indexed | 2024-04-24T18:16:39Z |
| publishDate | 2023-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Forecasting |
| spelling | doaj.art-32802a47aeee423287f386a5292162582024-03-27T13:41:22ZengMDPI AGForecasting2571-93942023-12-0161183510.3390/forecast6010002Improvement on Forecasting of Propagation of the COVID-19 Pandemic through Combining Oscillations in ARIMA ModelsEunju Hwang0Department of Applied Statistics, Gachon University, Seongnam-si 13120, Republic of KoreaDaily data on COVID-19 infections and deaths tend to possess weekly oscillations. The purpose of this work is to forecast COVID-19 data with partially cyclical fluctuations. A partially periodic oscillating ARIMA model is suggested to enhance the predictive performance. The model, optimized for improved prediction, characterizes and forecasts COVID-19 time series data marked by weekly oscillations. Parameter estimation and out-of-sample forecasting are carried out with data on daily COVID-19 infections and deaths between January 2021 and October 2022 in the USA, Germany, and Brazil, in which the COVID-19 data exhibit the strongest weekly cycle behaviors. Prediction accuracy measures, such as RMSE, MAE, and HMAE, are evaluated, and 95% prediction intervals are constructed. It was found that predictions of daily COVID-19 data can be improved considerably: a maximum of 55–65% in RMSE, 58–70% in MAE, and 46–60% in HMAE, compared to the existing models. This study provides a useful predictive model for the COVID-19 pandemic, and can help institutions manage their healthcare systems with more accurate statistical information.https://www.mdpi.com/2571-9394/6/1/2COVID-19periodic oscillationpredictiontime series model |
| spellingShingle | Eunju Hwang Improvement on Forecasting of Propagation of the COVID-19 Pandemic through Combining Oscillations in ARIMA Models Forecasting COVID-19 periodic oscillation prediction time series model |
| title | Improvement on Forecasting of Propagation of the COVID-19 Pandemic through Combining Oscillations in ARIMA Models |
| title_full | Improvement on Forecasting of Propagation of the COVID-19 Pandemic through Combining Oscillations in ARIMA Models |
| title_fullStr | Improvement on Forecasting of Propagation of the COVID-19 Pandemic through Combining Oscillations in ARIMA Models |
| title_full_unstemmed | Improvement on Forecasting of Propagation of the COVID-19 Pandemic through Combining Oscillations in ARIMA Models |
| title_short | Improvement on Forecasting of Propagation of the COVID-19 Pandemic through Combining Oscillations in ARIMA Models |
| title_sort | improvement on forecasting of propagation of the covid 19 pandemic through combining oscillations in arima models |
| topic | COVID-19 periodic oscillation prediction time series model |
| url | https://www.mdpi.com/2571-9394/6/1/2 |
| work_keys_str_mv | AT eunjuhwang improvementonforecastingofpropagationofthecovid19pandemicthroughcombiningoscillationsinarimamodels |