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Forecasting Covid-19: SARMA-ARCH approach
| dc.contributor.author | Kamalov, Firuz | |
| dc.contributor.author | Thabtah, Fadi | |
| dc.date.accessioned | 2022-02-14T15:24:45Z | |
| dc.date.available | 2022-02-14T15:24:45Z | |
| dc.date.copyright | © 2021 | |
| dc.date.issued | 2021-09 | |
| dc.description | This article is not available at CUD collection. The version of scholarly record of this article is published in Health and Technology (2021), available online at: https://doi.org/10.1007/s12553-021-00587-x | en_US |
| dc.description.abstract | Forecasting the number of Covid-19 cases is a crucial tool in public health policy. In this paper, we construct seasonal autoregressive moving average and autoregressive conditional heteroscedasticity models to forecast the spread of the infection in the UAE. While most of the existing literature is dedicated to forecasting the total number of infections, we endeavor to forecast the number of new infections which is a significantly more challenging task due to the greater volatility. Our models are based on a careful analysis of correlation plots and residual analysis. In addition, we employ highly accurate population data that leads to more reliable outcomes. The results reveal a high degree of accuracy of the proposed forecasting methods. The constructed models can be used by health officials to better anticipate and plan for new cases of Covid-19. © 2021, IUPESM and Springer-Verlag GmbH Germany, part of Springer Nature. | en_US |
| dc.identifier.citation | Kamalov, F., & Thabtah, F. (2021). Forecasting covid-19: SARMA-ARCH approach. Health and Technology, 11(5), 1139-1148. https://doi.org/10.1007/s12553-021-00587-x | en_US |
| dc.identifier.issn | 21907188 | |
| dc.identifier.uri | https://doi.org/10.1007/s12553-021-00587-x | |
| dc.identifier.uri | http://hdl.handle.net/20.500.12519/510 | |
| dc.language.iso | en | en_US |
| dc.publisher | Springer Science and Business Media Deutschland GmbH | en_US |
| dc.relation | Authors Affiliations : Kamalov, F., Department of Electrical Engineering, Canadian University Dubai, Dubai, United Arab Emirates; Thabtah, F., School of Digital Technologies, Manukau Institute of Technology, Auckland, New Zealand | |
| dc.relation.ispartofseries | Health and Technology;Volume 11, Issue 5 | |
| dc.rights | License to reuse the abstract has been secured from Springer Nature and Copyright Clearance Center. | |
| dc.rights.holder | Copyright : © 2021, IUPESM and Springer-Verlag GmbH Germany, part of Springer Nature. | |
| dc.rights.license | License Number : 5244621285898 License date : Feb 09, 2022 | |
| dc.rights.uri | https://s100.copyright.com/CustomerAdmin/PLF.jsp?ref=3b8988c9-4a5d-4031-99fc-1e873760cf89 | |
| dc.subject | AR-ARCH | en_US |
| dc.subject | Autoregression | en_US |
| dc.subject | Covid-19 | en_US |
| dc.subject | Forecasting | en_US |
| dc.subject | SARMA | en_US |
| dc.title | Forecasting Covid-19: SARMA-ARCH approach | en_US |
| dc.type | Article | en_US |