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?:abstract
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Covid-19 (novel coronavirus) was discovered in Wuhan, China in December 2019, and has since affected millions’ lives worldwide By 10th April 2020, Malaysia reported more than 4,000 outbreak cases, the highest in Southeast Asia Recently, a forecasting model was developed to measure and predict daily Covid-19 cases in Malaysia for the coming 10 days using previously-confirmed cases A Singular Spectrum Analysis-based forecasting model that discriminates noise in a time series trend is introduced The key concept of the proposed model, RF-SSA, is improving the efficiency of recurrent SSA by establishing L and r parameters via several tests The RF-SSA model assessment is based on the World Health Organization’s official Covid-19 data to predict the daily confirmed cases after 10th April until 20th April, 2020 These results show that the parameter L= 4 (T/20) for RF-SSA model was suitable for short time series outbreak data and the appropriate number of eigentriples to obtain is important as it influences the forecasting result Evidently, the RF-SSA has over-forecasted the cases by 0 36% This indicates RF-SSA’s competence to predict the impending number of Covid-19 cases Nevertheless, enhanced RF-SSA algorithm should to be developed for higher effectivity in capturing any extreme data changes © 2020, World Academy of Research in Science and Engineering All rights reserved
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