Sinusoidal Predictive Model of Nigeria Petroleum Motor Spirit (PMS) Price
Article Sidebar
Page Numbers:
580-592
Digital Object Identifier:
10.58578/ajstea.v4i4.10310
Viewed: 0 times
Downloaded: 0 times
LYAS Publisher cordially invites qualified professionals to serve as Editors or Reviewers. Your expertise will make an important contribution to maintaining and strengthening the academic quality of our publications. Interested applicants are kindly requested to complete the application form at the following link: Editors & Reviewers
Please feel free to contact us if you need any further information about the submission process or if you have any additional questions.
Authors:Akpienbi I. O.1*, Bello M.I.2, Atureta M.S.3, Barde A.4
Copyright :Authors retain copyright and grant the journal right of first publication.
Main Article Content
Abstract
Petroleum Motor Spirit (PMS) prices in Nigeria exhibit substantial fluctuations that challenge conventional linear forecasting approaches and complicate policy and market planning. This study applies a seventh-order sinusoidal wave-function model to analyze and predict PMS price dynamics in Nigeria. Annual PMS price data were modeled using a sinusoidal framework designed to capture the recurring oscillatory behavior associated with regulated fuel-pricing systems. A logarithmic transformation and the Least Squares Method (LSM) were employed to reduce data dispersion and obtain robust parameter estimates. Model performance was validated through correlation analysis between observed and predicted prices, while pilot testing was conducted to assess the model’s reliability, stability, and predictive relevance. The results demonstrate a high degree of association between actual PMS prices and model estimates, indicating that the sinusoidal structure adequately represents the cyclical behavior of PMS prices during the study period. The findings further suggest that PMS price variations are not explained primarily by linear temporal progression but follow a wave-like pattern consistent with periodic policy adjustments, market interventions, and regulatory decisions. The proposed model therefore provides a useful analytical framework for interpreting PMS price fluctuations and forecasting future price movements within Nigeria’s fuel-pricing regime. This study contributes a nonlinear modeling approach that may support evidence-based fuel-pricing analysis, regulatory planning, and policy decision-making.
Citation Metrics:
Downloads
Citation Metrics & Similar Scopus Articles
-
Yamamiya A. (2027)Recent Development of Endo-hepatologyDen Open, 7(1)
-
Vural G. (2027)Determining the robust drivers of CO2 emissions in Africa: Machine learning and panel econometric evidence on the Environmental Kuznets Curve hypothesisUnconventional Resources, 17
-
Zhao H. (2027)Short-term wind power prediction using CEEMDAN-SVMD and FE-adaptive fusion TCN-transformerElectric Power Systems Research, 265
Article Details
Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
References
Amelia, R., & Wulandhari, L. A. (2025). Crude oil price forecasting using LSTM and GRU feature extractor and machine learning regressor. Journal of Advances in Information Technology, 16(8), 1100–1117. https://doi.org/10.12720/jait.16.8.1100-1117
Balogun, T. F. (2015). Mapping impacts of crude oil theft and illegal refineries on mangrove of the Niger Delta of Nigeria with remote sensing technology. Mediterranean Journal of Social Sciences, 6(3), 150–155. https://doi.org/10.5901/mjss.2015.v6n3p150
Bharathi, S., & Sujatha, P. (2025). Oil and gas industry price prediction using hybrid machine learning techniques. Journal of Information Systems Engineering and Management, 10(41s), 56–68. https://doi.org/10.52783/jisem.v10i41s.7652
Bosler, F. T. (2010). Models for oil price prediction and forecasting [Master’s thesis, San Diego State University].
Cohen, G. (2025). A comprehensive study on short-term oil price forecasting using econometric and machine learning techniques. Machine Learning and Knowledge Extraction, 7(4), Article 127. https://doi.org/10.3390/make7040127
Egbewole, Z. T., & Rotowa, O. J. (2018). Hike in pump price: Major doom to Nigerian forest. Journal of Energy, Environmental & Chemical Engineering, 3(2), 19–26. https://doi.org/10.11648/j.jeece.20180302.11
Kadafa, A. A. (2012). Oil exploration and spillage in the Niger Delta of Nigeria. Civil and Environmental Research, 2(3), 38–51. https://www.iiste.org/Journals/index.php/CER/article/view/1789
Kumar, K. (2025). Forecasting crude oil prices using reservoir computing models. Computational Economics, 66(3), 2543–2563. https://doi.org/10.1007/s10614-024-10797-w
Li, M., Xiao, Y., Ding, S., Zhang, Q., Xiong, H., & Ding, J. (2025). Crude oil price fluctuation forecasting incorporating news sentiment based on improved sentiment lexicon. Journal of King Saud University Computer and Information Sciences, 37, Article 262. https://doi.org/10.1007/s44443-025-00289-8
Moshiri, S., & Foroutan, F. (2006). Forecasting nonlinear crude oil futures prices. The Energy Journal, 27(4), 81–96. https://doi.org/10.5547/ISSN0195-6574-EJ-Vol27-No4-4
Ndigwe, C. (2022a, June 14). Angola overtakes Nigeria as Africa’s biggest oil producer. BusinessDay. https://businessday.ng/energy/article/angola-overtakes-nigeria-as-africas-biggest-oil-producer/
Ndigwe, C. (2022b, December 14). Nigeria pumped 1.18m bpd oil in November. BusinessDay. https://businessday.ng/energy/article/nigeria-pumped-1-18m-bpd-oil-in-november/
Nguyen, T. H., Nguyen, N. M. T., Le, T. P. T., Nguyen, T. H. P., & Hoang, T. H. (2025). Application of ARIMA and LSTM models for crude oil price forecasting. International Journal of Scientific Engineering and Science, 9(4), 31–34. https://ijses.com/wp-content/uploads/2025/04/121-IJSES-V9N3.pdf
Ogwumu, O. D., Ataribu, O. S., Akpienbi, I. O., Otti, E. E., Ogofotha, M. O., Philemon, M. E., & Shaiki, I. R. (2022a). A mathematical model for estimating an intelligence quotient (IQ) of retiree and humans above 65 years (A study of Federal University Wukari community members of Nigeria). International Journal of Engineering and Manufacturing, 12(2), 41–51. https://doi.org/10.5815/ijem.2022.02.05
Ogwumu, O. D., & Ataribu, O. S. (2022b). Determination of a better non-linear mathematical model with trigonometric sinusoidal behaviour for the pricing of local rice in Nigeria market. Engineering Mathematics Letters, 2022, 1–23. https://doi.org/10.28919/eml/6643
Okere, R. (2017, April 18). Nigeria loses Africa’s top oil producer position to Angola. The Guardian Nigeria. https://guardian.ng/news/nigeria-loses-africas-top-oil-producer-position-to-angola/
Omoregie, U. (2019). Nigeria’s petroleum sector and GDP: The missing oil refining link. Journal of Advances in Economics and Finance, 4(1), 1–8. https://doi.org/10.22606/jaef.2019.41001
Rao, A., Sharma, G. D., Tiwari, A. K., Hossain, M. R., & Dev, D. (2025). Crude oil price forecasting: Leveraging machine learning for global economic stability. Technological Forecasting and Social Change, 216, Article 124133. https://doi.org/10.1016/j.techfore.2025.124133
Sanni, I. M. (2014). The implications of price changes on petroleum products distribution in Gwagwalada Abuja, Nigeria. Journal of Energy Technologies and Policy, 4(7), 1–16. https://www.iiste.org/Journals/index.php/JETP/article/view/14278
Tuo, J., & Sun, Y. (2011). Summary of world oil price forecasting model. In 2011 Fourth International Symposium on Knowledge Acquisition and Modeling (pp. 327–330). IEEE. https://doi.org/10.1109/KAM.2011.94