International Journal of Electrical and Computer Engineering (IJECE) Vol. 15, No. 1, February 2025, pp. 728~740 ISSN: 2088-8708, DOI: 10.11591/ijece.v15i1.pp728-740  728 Journal homepage: http://ijece.iaescore.com Enhancing PETRONAS share price forecasts: a hybrid Holt integrated moving average Nurin Qistina Mohamad Fozi 1 , Nurhasniza Idham Abu Hasan 1 , Azlan Abdul Aziz 2 , Siti Meriam Zahari 3 , Mogana Darshini Ganggayah 4 1 College of Computing, Informatics and Mathematics, Universiti Teknologi MARA Perak Branch, Tapah, Malaysia 2 College of Computing, Informatics and Mathematics, Universiti Teknologi MARA Perlis Branch, Arau, Malaysia 3 College of Computing, Informatics and Mathematics, Universiti Teknologi MARA Selangor, Shah Alam, Malaysia 4 School of Business, Monash University Malaysia, Selangor, Malaysia Article Info ABSTRACT Article history: Received Apr 4, 2024 Revised Sep 4, 2024 Accepted Oct 1, 2024 Understanding the variations in PETRONAS share price over time is important for improving the forecast accuracy of PETRONAS share prices to provide stakeholders with reliable analyses for future market predictions. Therefore, the main objective of this study is to improve the accuracy of PETRONAS share price by utilizing a hybrid Holt method with the moving average (MA) from the Box-Jenkins model. Holt's method will address linear trends for non-stationary data, while MA will analyze residual aspects of the data. This combination transforms non-stationary data into stationary by removing noise and averaging out fluctuations. The secondary data used in this study consists of daily observation from bursa Malaysia, the official national stock exchange of Malaysia, covering the period from January 3, 2000, to October 2, 2023. The study encompasses both low and high share price scenarios. The models’ performance was compared using various error metrics across different training and testing splits. The findings highlight that the proposed hybrid [Holt–MA] model called Holt integrated moving average (HIMA) improves the accuracy of forecasting model with the smallest errors for both daily low and high share price. The HIMA model demonstrates significant potential, particularly in reducing residuals and improving prediction accuracy. Keywords: Accuracy Autoregressive integrated moving average Damped trend method Holt method Time series forecasting This is an open access article under the CC BY-SA license. Corresponding Author: Nurhasniza Idham Abu Hasan College of Computing, Informatics and Mathematics, Universiti Teknologi MARA Perak Branch 35400, Tapah Road, Perak Darul Ridzuan, Malaysia Email: nurhasniza@uitm.edu.my 1. INTRODUCTION Petroliam Nasional Berhad, known as PETRONAS is Malaysia’s foremost integrated oil and gas enterprise and has become a major player in the global energy sector. This sector operates within a milieu shaped by fluctuations in international oil prices, geopolitical complexities, technological advancements, and sustainability imperatives [1]. The share price of PETRONAS encapsulates the cumulative impact of these diverse factors, serving as a reflection not only of the company’s financial performance but also of broader trends within the energy sector. Given its fundamental role in underpinning the nation’s economic framework, the volatility of PETRONAS’ share price holds significant ramifications for Malaysia’s financial markets and broader economic stability [2]. The interplay of these factors, combined with the company’s strategic initiatives and financial performance, contributes to the intricate mosaic of its market valuation [3]. As Malaysia positions itself as a regional energy hub, PETRONAS share price is not only reflective of its