Forecasting Crude Oil Prices Using an ARIMA-ANN Hybrid Model

Authors

Hleil Alrweili, Department of Mathematics, Faculty of Arts and Science, Northern Border University, Rafha, Saudi ArabiaFollow
Haitham Fawzy, Department of Applied Statistics and Econometrics, Faculty of Graduate Studies for Statistical Research, Cairo, EgyptFollow

Abstract

This paper aims to use a hybrid ARIMA-ANN model for time series forecasting by combining Auto Regressive Integrated Moving Average (ARIMA) model and the Artificial Neural Networks (ANN). The hybrid ARIMA-ANN model is flexible enough to capture two kinds of time series: the linear model that can only model the linear relationship and nonlinear that can only model the nonlinear relationship. The time series data for the Crude Oil (petroleum) Monthly Price - Saudi Riyal per Barrel was used during the period from Jul-2001 to May-2021, which represents 239 observations. The first 215 observations are used as train series and the last 24 observations are used as testing series. The accuracy measures, Mean Square Error (MSE), Mean Absolute Percentage Error (MAPE), and Mean Absolute Error (MAE) for the hybrid combination ARIMA and ANN were compared against ARIMA and ANN methods. The results indicate significant improvement in (MSE, MAPE, and MAE) values for the hybrid ARIMA-ANN method.

Digital Object Identifier (DOI)

http://dx.doi.org/10.18576/jsap/110308

Recommended Citation

Alrweili, Hleil and Fawzy, Haitham (2022) "Forecasting Crude Oil Prices Using an ARIMA-ANN Hybrid Model," Journal of Statistics Applications & Probability: Vol. 11: Iss. 3, Article 8.
DOI: http://dx.doi.org/10.18576/jsap/110308
Available at: https://digitalcommons.aaru.edu.jo/jsap/vol11/iss3/8