Modeling and forecasting petroleum motor spirit (PMS) prices in Nigeria: An ARIMA-based time series analysis
DOI:
https://doi.org/10.64497/jssci.82Keywords:
Petroleum Motor Spirit (PMS),, Autocorrelation Function (ACF), Partial Autocorrelation Function (PACF), Autoregressive Integrated Moving Average Model (ARIMA), StationarityAbstract
This study investigated the trend and forecasting of petroleum motor spirit (PMS) prices in Nigeria using the autoregressive integrated moving average (ARIMA) modeling approach. Monthly PMS price data were analyzed to identify underlying trends and stationarity. Preliminary analysis using Augmented Dickey-Fuller (ADF) tests confirmed stationarity at the first difference. Model selection was guided by the examination of autocorrelation function (ACF) and partial autocorrelation function (PACF) plots, resulting in the estimation of ARIMA(5,1,0), ARIMA(0,1,5), and ARIMA(5,1,5) models. Among these, ARIMA(0,1,5) demonstrated superior performance with the lowest Akaike information criterion (AIC) and Bayesian information criterion (BIC), and residual diagnostics confirmed model adequacy. The model captures the historical dynamics of PMS prices and offers reliable forecasts that can inform policymakers and stakeholders in planning for fuel price stabilization and economic management.
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Copyright (c) 2025 P.O. Evans, M.O. Abifarin, E.A. Ali
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