ORCID Profile
0000-0003-4372-1572
Current Organisations
City, University of London
,
University of Sussex
,
University of Texas Rio Grande Valley
,
Anglia Ruskin University
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Publications
Designing for Transformative Futures: Creative Practice, Social Change and Climate Emergency
Publisher: ACM
Date: 22-06-2021
Determining an effective short term COVID-19 prediction model in ASEAN countries
Publisher: Springer Science and Business Media LLC
Date: 24-03-2022
DOI: 10.1038/S41598-022-08486-5
Abstract: The challenge of accurately short-term forecasting demand is due to model selection and the nature of data trends. In this study, the prediction model was determined based on data patterns (trend data without seasonality) and the accuracy of prediction measurement. The cumulative number of COVID-19 affected people in some ASEAN countries had been collected from the Worldometers database. Three models [Holt’s method, Wright’s modified Holt’s method, and unreplicated linear functional relationship model (ULFR)] had been utilized to identify an efficient model for short-time prediction. Moreover, different smoothing parameters had been tested to find the best combination of the smoothing parameter. Nevertheless, using the day-to-day reported cumulative case data and 3-days and 7-days in advance forecasts of cumulative data. As there was no missing data, Holt’s method and Wright’s modified Holt’s method showed the same result. The text-only result corresponds to the consequences of the models discussed here, where the smoothing parameters (SP) were roughly estimated as a function of forecasting the number of affected people due to COVID-19. Additionally, the different combinations of SP showed erse, accurate prediction results depending on data volume. Only 1-day forecasting illustrated the most efficient prediction days (1 day, 3 days, 7 days), which was validated by the Nash–Sutcliffe efficiency (NSE) model. The study also validated that ULFR was an efficient forecasting model for the efficient model identifying. Moreover, as a substitute for the traditional R-squared, the study applied NSE and R-squared (ULFR) for model selection. Finally, the result depicted that the prediction ability of ULFR was superior to Holt’s when it is compared to the actual data.
Computational Statistical Methodologies and Modeling for Artificial Intelligence
Publisher: CRC Press
Date: 22-03-2023
Related Organisations
City, University Of London
Location: United Kingdom of Great Britain and Northern Ireland
University Of Sussex
Location: United Kingdom of Great Britain and Northern Ireland
University Of Texas Rio Grande Valley
Location: United States of America
Anglia Ruskin University
Location: United Kingdom of Great Britain and Northern Ireland
Goldsmiths University Of London Department Of Sociology
Location: United Kingdom of Great Britain and Northern Ireland
Microsoft Research Ltd
Location: United Kingdom of Great Britain and Northern Ireland
Related Funding Activities
No related grants have been discovered for Omar Sharif.