Modelling COViD-19 Daily New Cases using GSTARARIMA Forecasting Method: Case Study on Five Malaysian States

Abdullah, Siti Nabilah Syuhada and Shabri, Ani and Saeed, Faisal and Samsudin, Ruhaidah and Basurra, Shadi (2023) Modelling COViD-19 Daily New Cases using GSTARARIMA Forecasting Method: Case Study on Five Malaysian States. Lecture Notes on Data Engineering and Communications Technologies. ISSN 2367-4520

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Abstract

On March 11, 2020, the World Health Organization declared COVID-19 to be a pandemic after the number of confirmed cases had surpassed 118,000 cases in more than 110 countries worldwide. To aid decision-makers in battling the epidemic, accurate modelling and forecasting of the spread of confirmed and recovered COVID-19 cases is essential. The non-linear patterns that are frequently seen in these situations have inspired us to create a system that can record such alterations. A hybrid method was approached in this study. Using hybrid models or combining several models has been a common practice to increase forecasting accuracy. Here, an error dataset was obtained from the GSTAR model previously and the error data for each location was modelled using ARIMA model. The final goal of this research is to develop a technique for predicting new COVID 19 cases using a hybrid GSTAR-ARIMA model. From March 16, 2020, to July 23, 2021, a case study was conducted on the number of daily confirmed COVID-19 cases in five Malaysian states. Global Change Data Lab at Oxford University furnished the dataset. GTAR-ARIMA with Uniform weights proves to be a viable forecasting option, ultimately proving to be the best model for forecasting daily new confirmed cases of COVID-19.

Item Type:	Article
Identification Number:	10.1007/978-3-031-36258-3_39
Dates:	Date Event 1 October 2022 Accepted 17 August 2023 Published Online
Subjects:	CAH11 - computing > CAH11-01 - computing > CAH11-01-01 - computer science CAH11 - computing > CAH11-01 - computing > CAH11-01-05 - artificial intelligence
Divisions:	Faculty of Computing, Engineering and the Built Environment > College of Computing
Depositing User:	Faisal Saeed
Date Deposited:	29 Nov 2022 10:48
Last Modified:	05 Oct 2023 14:14
URI:	https://www.open-access.bcu.ac.uk/id/eprint/13962

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