Non-Sinusoidal Predictive Model of Premium Motor Spirit (PMS) in Nigerian Fuel Price Hike

Main Article Content

Akpienbi I. O.
Bello M.I.
Atureta M.S.
Barde A.

Abstract

This study investigates the dynamics of Premium Motor Spirit (PMS) prices in Nigeria using a non-sinusoidal high-order mathematical model applied to annual data spanning 1990–2022. The proposed modelling framework is designed to represent long-term price growth behaviour and structural progression rather than periodic or cyclical movements. Model parameters were estimated using the least squares estimation technique to ensure optimal fitting of the observed price series. The adequacy of the model was evaluated through validation error metrics and correlation analysis, providing quantitative measures of goodness-of-fit and predictive reliability. The findings indicate that the non-sinusoidal high-order model effectively represents the sustained upward trajectory of PMS prices over the study period, reflecting gradual economic adjustments, inflationary pressures, and long-term policy influences. However, deviations between observed and model-generated prices were evident during periods characterized by abrupt policy interventions, subsidy reforms, and regulatory shocks, which introduced short-term irregularities into the price structure. Despite these disturbances, the model maintained a strong capacity to describe the overall price evolution and underlying trend of PMS pricing in Nigeria. This study contributes to energy price modelling literature by demonstrating the relevance of non-sinusoidal growth-based approaches for analysing long-term energy price behaviour in regulated and policy-sensitive economic environments.

Downloads

Download data is not yet available.

Scopus Citation Data

Data source Crossref
0
citations
Check Secondary Documents in Scopus
Open this article in Scopus, then check the Secondary documents tab. Use Manual Citation Fallback only for counts you have verified manually.
Open in Scopus
Similar Scopus Articles
Scopus
  1. Gottlieb A. (2027)
    Effects of sleep deprivation on cognitive-motor functions and adaptive skill learning among medical residents across 26h night shifts
    Applied Ergonomics, 138
  2. Ding R. (2027)
    Toward clean and efficient PODE/ammonia RCCI engines: from system optimization to experimental validation with insights from reaction pathway analysis
    Fuel, 428
  3. Su Z. (2027)
    Long-lived 2700 K W-Re thermocouples enabled by WSi2/ZrO2-Al2O3-SiO2 multilayer barriers
    Journal of Materials Science and Technology, 277, 265-274

Article Details

How to Cite
I. O., A., M.I., B., M.S., A., & A., B. (2026). Non-Sinusoidal Predictive Model of Premium Motor Spirit (PMS) in Nigerian Fuel Price Hike. Mikailalsys Journal of Mathematics and Statistics, 4(2), 419-431. https://doi.org/10.58578/mjms.v4i2.9367

References

Adati, A. K. (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

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), 150–153. 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

Isyaka, M. S. (2014). The implications of price changes on petroleum products distribution in Gwagwalada, Abuja, Nigeria. Journal of Energy Technologies and Policy, 4(7), 1–15. https://www.iiste.org/Journals/index.php/JETP/article/view/14278

Kumar, K. (2025). Forecasting crude oil prices using reservoir computing models. Computational Economics, 66, 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. (2022, 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. (2022, 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. (2022). 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. (2022). 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, Article 1. https://doi.org/10.28919/eml/6643

Okere, R. (2017, April 18). Nigeria loses Africa’s top oil producer position to Angola. The Guardian. 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

Tuo, J., & Yanbing, S. (2011). Summary of world oil price forecasting model. In 2011 Fourth International Symposium on Knowledge Acquisition and Modeling (KAM) (pp. 327–330). IEEE. https://doi.org/10.1109/KAM.2011.94


Explore Our Journals
Find the most suitable journal for your research. If this journal does not fully align with the scope of your manuscript, we invite you to explore our wider portfolio of journals covering diverse fields of study. Please select one of the journals below to identify the most appropriate publication platform for your work.