Forecasting Covid-19: SARMA-ARCH approach Kamalov, Firuz Thabtah, Fadi 2022-02-14T15:24:45Z 2022-02-14T15:24:45Z © 2021 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: 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. en_US
dc.identifier.issn 21907188
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.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
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