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

doi:10.1016/j.mex.2025.103347

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

MethodsX. 2025 May 1:14:103347. doi: 10.1016/j.mex.2025.103347. eCollection 2025 Jun.

Authors

Azlan Abdul Aziz^{1

2}, Zuriani Mustaffa³, Suzilah Ismail⁴, Nor Azriani Mohamad Nor^{1

2}, Nurin Qistina Mohamad Fozi⁵

Affiliations

¹ Faculty of Computer and Mathematical Sciences, Universiti Teknologi MARA (UiTM) Cawangan Perlis, Arau, 02600 Perlis, Malaysia.
² Statistical Analytics, Forecasting & Innovation (SAFI) Research Interest Group, Universiti Teknologi MARA Cawangan Perlis, Arau 02600 Perlis, Malaysia.
³ Faculty of Computing, Universiti Malaysia Pahang Al-Sultan Abdullah, Pekan 26600 Pahang, Malaysia.
⁴ School of Quantitative Sciences, Universiti Utara Malaysia UUM Sintok, Kedah, Malaysia.
⁵ Faculty of Computer and Mathematical Sciences, Universiti Teknologi MARA (UiTM) Cawangan Perak, Kampus Tapah, 35400 Tapah Road, Perak, Malaysia.

Abstract

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 time-series 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 %.

Keywords: BMO; Forecast accuracy; Forecasting model; Parameter optimization; SES; Single Exponential Smoothing – Barnacles Mating Optimizer (SES-BMO); Time series analysis.