Forecasting COVID-19 Confirmed Cases in Ghana: A Model Selection Approach
Abstract: This study seeks to determine an appropriate statistical technique for forecasting the cumulated confirm cases of Coronavirus in Ghana. Cumulated daily data spanning from March 12, 2020, to August 04, 2020, was retrieved from the Center for Systems Science and Engineering at Johns Hopkins University. Four statistical forecasting techniques: Autoregressive Integrated Moving Average, Artificial Neural Network, Exponential smoothing and Autoregressive Fractional Integrated Moving Average were fitted to the COVID-19 series. Their respective forecast accuracy measures were compared to select the appropriate technique for forecasting the COVID-19 cases. Our findings revealed that the ARFIMA technique was a suitable statistical model for predicting COVID-19 cases in Ghana. The "best" model for forecasting is ARFIMA (2, 0.49, 4) which passed all the needed diagnostic tests. An unequal weight was estimated to derive a combined model for all four forecasting techniques. A 149-cumulated daily
- Location
-
Deutsche Nationalbibliothek Frankfurt am Main
- Extent
-
Online-Ressource
- Language
-
Englisch
- Notes
-
Veröffentlichungsversion
begutachtet (peer reviewed)
In: Path of Science ; 7 (2021) 2 ; 4001-4010
- Classification
-
Wirtschaft
- Event
-
Veröffentlichung
- (where)
-
Mannheim
- (who)
-
SSOAR, GESIS – Leibniz-Institut für Sozialwissenschaften e.V.
- (when)
-
2021
- Creator
-
Twumasi-Ankrah, Sampson
Owusu, Michael
Appiah, Simon Kojo
Pels, Wilhemina Adoma
Arthur, Doris
- DOI
-
10.22178/pos.67-2
- URN
-
urn:nbn:de:101:1-2022071906363008367553
- Rights
-
Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Last update
-
15.08.2025, 7:31 AM CEST
Data provider
Deutsche Nationalbibliothek. If you have any questions about the object, please contact the data provider.
Associated
- Twumasi-Ankrah, Sampson
- Owusu, Michael
- Appiah, Simon Kojo
- Pels, Wilhemina Adoma
- Arthur, Doris
- SSOAR, GESIS – Leibniz-Institut für Sozialwissenschaften e.V.
Time of origin
- 2021
Other Objects (12)
COVID-19 confirmed patients with negative antibodies results
On the selection of forecasting models
Forecasting VARs, model selection, and shrinkage
On the selection of forecasting models
Forecasting VARs, model selection, and shrinkage
Rationalizable partition-confirmed equilibrium
Cerebral Vasculitis in a COVID-19 Confirmed Postpartum Patient: A Case Report
Diagnostic value of abdominal sonography in confirmed COVID-19 intensive care patients
Variable selection and inference for multi-period forecasting problems
eVision: Epidemic Forecasting on COVID-19
Automated Machine Learning for COVID-19 Forecasting
Management of suspected and confirmed COVID‐19 (SARS‐CoV‐2) vaccine hypersensitivity
COVID-19 confirmed patients with negative antibodies results
On the selection of forecasting models
Forecasting VARs, model selection, and shrinkage
On the selection of forecasting models
Forecasting VARs, model selection, and shrinkage
Rationalizable partition-confirmed equilibrium
Cerebral Vasculitis in a COVID-19 Confirmed Postpartum Patient: A Case Report
Diagnostic value of abdominal sonography in confirmed COVID-19 intensive care patients
Variable selection and inference for multi-period forecasting problems
eVision: Epidemic Forecasting on COVID-19
Automated Machine Learning for COVID-19 Forecasting
Management of suspected and confirmed COVID‐19 (SARS‐CoV‐2) vaccine hypersensitivity
COVID-19 confirmed patients with negative antibodies results
On the selection of forecasting models
Forecasting VARs, model selection, and shrinkage
On the selection of forecasting models
Forecasting VARs, model selection, and shrinkage
Rationalizable partition-confirmed equilibrium
Cerebral Vasculitis in a COVID-19 Confirmed Postpartum Patient: A Case Report
Diagnostic value of abdominal sonography in confirmed COVID-19 intensive care patients
Variable selection and inference for multi-period forecasting problems
eVision: Epidemic Forecasting on COVID-19
Automated Machine Learning for COVID-19 Forecasting