Predicting Crude Oil Price in Nigeria with Machine Learning Models

Abstract

Background: The rise in crude oil prices yields serious consequences for both oil-producing and non-oil-producing countries. The increase in global commodity prices contributes to the financial income and foreign exchange reserves of oil-exporting countries. However, countries such as Nigeria that sell crude oil and purchase refined fuel confront more complicated situations. To this end, there exists a need to obtain a robust prediction model for the crude oil price of Nigeria. Objective: This study is to determine the best model among the machine-learning time series models considered to predict crude oil prices in Nigeria. Methods: The alternative models were the auto-regressive integrated moving average model, Naive Bayes, Holtwinter trend model, exponential smoothing model, and neural network autoregressive (NNETAR) model. The prediction criteria adopted for model screening were the root mean square error (RMSE), mean absolute error (MAE) and mean absolute percentage error (MAPE). Daily crude oil prices in dollars obtained from the Central Bank of Nigeria were used for analysis spanning from October 1, 2009 to March 22, 2022 with 2836 data points. Results: The NNETAR model showed the minimum RMSE, MAE, and MAPE for cross-validation sets considered. Conclusion: The NNETAR model was recommended for the prediction of crude oil prices in Nigeria.