A hybrid simple exponential smoothing-barnacles mating optimization approach for parameter estimation: Enhancing COVID-19 forecasting in Malaysia

Single or simple exponential smoothing (SES) is a time series forecasting model popular among researchers due to its simplicity and ease of use. SES only requires one smoothing parameter, alpha, to control how quickly the influence of past observations decreases. However, SES is seen to underperform...

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Main Authors: Azlan, Abdul Aziz, Zuriani, Mustaffa, Suzilah, Ismail, Nor Azriani, Mohamad Nor, Nurin Qistina, Mohamad Fozi
Format: Article
Language:English
Published: Elsevier B.V. 2025
Subjects:
Online Access:https://umpir.ump.edu.my/id/eprint/45604/
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author Azlan, Abdul Aziz
Zuriani, Mustaffa
Suzilah, Ismail
Nor Azriani, Mohamad Nor
Nurin Qistina, Mohamad Fozi
author_facet Azlan, Abdul Aziz
Zuriani, Mustaffa
Suzilah, Ismail
Nor Azriani, Mohamad Nor
Nurin Qistina, Mohamad Fozi
author_sort Azlan, Abdul Aziz
building UMP Institutional Repository
collection Online Access
description Single or simple exponential smoothing (SES) is a time series forecasting model popular among researchers due to its simplicity and ease of use. SES only requires one smoothing parameter, alpha, to control how quickly the influence of past observations decreases. However, SES is seen to underperform compared to other models due to parameter selection and initial value setting. Therefore, this study aims to propose a new hybrid model, the Single Exponential Smoothing (SES)-Barnacles Mating Optimization (BMO) algorithm, to estimate the optimal smoothing parameter alpha and initial value that can improve the percentage of forecast accuracy. Some of the highlights of the proposed method are: • A new hybrid model, SES-BMO, has successfully estimated the optimal initial value and smoothing parameter simultaneously with a high forecast accuracy (90.2 %). • The data splitting ratio 80:20 or 75:25 is unsuitable for research cases requiring immediate action and decision, such as the COVID-19 pandemic. Thus, implementing Repeated timeseries cross-validation (RTS-CV) is a good practice in model validation. • The average 8-day forecast accuracy is 90.2 %. The lowest and highest forecast accuracy was 83.7 % and 98.8 %.
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institution Universiti Malaysia Pahang
institution_category Local University
language English
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publishDate 2025
publisher Elsevier B.V.
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spelling ump-456042025-09-10T01:50:47Z https://umpir.ump.edu.my/id/eprint/45604/ A hybrid simple exponential smoothing-barnacles mating optimization approach for parameter estimation: Enhancing COVID-19 forecasting in Malaysia Azlan, Abdul Aziz Zuriani, Mustaffa Suzilah, Ismail Nor Azriani, Mohamad Nor Nurin Qistina, Mohamad Fozi QA75 Electronic computers. Computer science RA0421 Public health. Hygiene. Preventive Medicine Single or simple exponential smoothing (SES) is a time series forecasting model popular among researchers due to its simplicity and ease of use. SES only requires one smoothing parameter, alpha, to control how quickly the influence of past observations decreases. However, SES is seen to underperform compared to other models due to parameter selection and initial value setting. Therefore, this study aims to propose a new hybrid model, the Single Exponential Smoothing (SES)-Barnacles Mating Optimization (BMO) algorithm, to estimate the optimal smoothing parameter alpha and initial value that can improve the percentage of forecast accuracy. Some of the highlights of the proposed method are: • A new hybrid model, SES-BMO, has successfully estimated the optimal initial value and smoothing parameter simultaneously with a high forecast accuracy (90.2 %). • The data splitting ratio 80:20 or 75:25 is unsuitable for research cases requiring immediate action and decision, such as the COVID-19 pandemic. Thus, implementing Repeated timeseries cross-validation (RTS-CV) is a good practice in model validation. • The average 8-day forecast accuracy is 90.2 %. The lowest and highest forecast accuracy was 83.7 % and 98.8 %. Elsevier B.V. 2025 Article PeerReviewed pdf en cc_by_nc_4 https://umpir.ump.edu.my/id/eprint/45604/1/A%20hybrid%20simple%20exponential%20smoothing-barnacles%20mating.pdf Azlan, Abdul Aziz and Zuriani, Mustaffa and Suzilah, Ismail and Nor Azriani, Mohamad Nor and Nurin Qistina, Mohamad Fozi (2025) A hybrid simple exponential smoothing-barnacles mating optimization approach for parameter estimation: Enhancing COVID-19 forecasting in Malaysia. MethodsX, 14 (103347). pp. 1-11. ISSN 2215-0161. (Published) https://doi.org/10.1016/j.mex.2025.103347 https://doi.org/10.1016/j.mex.2025.103347 https://doi.org/10.1016/j.mex.2025.103347
spellingShingle QA75 Electronic computers. Computer science
RA0421 Public health. Hygiene. Preventive Medicine
Azlan, Abdul Aziz
Zuriani, Mustaffa
Suzilah, Ismail
Nor Azriani, Mohamad Nor
Nurin Qistina, Mohamad Fozi
A hybrid simple exponential smoothing-barnacles mating optimization approach for parameter estimation: Enhancing COVID-19 forecasting in Malaysia
title A hybrid simple exponential smoothing-barnacles mating optimization approach for parameter estimation: Enhancing COVID-19 forecasting in Malaysia
title_full A hybrid simple exponential smoothing-barnacles mating optimization approach for parameter estimation: Enhancing COVID-19 forecasting in Malaysia
title_fullStr A hybrid simple exponential smoothing-barnacles mating optimization approach for parameter estimation: Enhancing COVID-19 forecasting in Malaysia
title_full_unstemmed A hybrid simple exponential smoothing-barnacles mating optimization approach for parameter estimation: Enhancing COVID-19 forecasting in Malaysia
title_short A hybrid simple exponential smoothing-barnacles mating optimization approach for parameter estimation: Enhancing COVID-19 forecasting in Malaysia
title_sort hybrid simple exponential smoothing-barnacles mating optimization approach for parameter estimation: enhancing covid-19 forecasting in malaysia
topic QA75 Electronic computers. Computer science
RA0421 Public health. Hygiene. Preventive Medicine
url https://umpir.ump.edu.my/id/eprint/45604/
https://umpir.ump.edu.my/id/eprint/45604/
https://umpir.ump.edu.my/id/eprint/45604/