Current Issue
Romanian Journal of Information Technology and Automatic Control / Vol. 35, No. 4, 2025
Comparative analysis of AI models for crude oil price forecasting: ARIMA, SARIMAX, and LSTM
Faizah ALSHAMMARI, Nahla ALJOJO, Modhi ALSHAMMARI, Amnah ALSAYED, Mona ALMALKI
One of the most vital strategic commodities with particular attention and popularity is crude oil since it affects people's daily life and is used in several sectors of industry. Globally, political and economic events affect crude oil prices constantly. Aiming to avoid financial losses or guaranteed future profits, many oil-related companies study the market to forecast prices. This work forecasts future crude oil prices using time series approaches. The Autoregressive Integrated Moving Average (ARIMA), Seasonal Autoregressive Integrated Moving Average with eXogenous (SARIMAX), and Long-Short-Term Memory (LSTM) models are applied to a real dataset spanning 12,055 days of oil prices. The three algorithms are to be compared in order to find the most accurate model for a crude oil price projection. The root mean squared error (RMSE) and mean absolute percentage error (MAPE) help to assess accuracy. With an RMSE of 0.02 and a MAPE of 2.6% SARIMAX shows to be better than the other models.
Keywords:
Crude Oil, SARIMAX, ARIMA, LSTM, Forecasting.
CITE THIS PAPER AS:
Faizah ALSHAMMARI,
Nahla ALJOJO,
Modhi ALSHAMMARI,
Amnah ALSAYED,
Mona ALMALKI,
"Comparative analysis of AI models for crude oil price forecasting: ARIMA, SARIMAX, and LSTM",
Romanian Journal of Information Technology and Automatic Control,
ISSN 1220-1758,
vol. 35(4),
pp. 7-20,
2025.
https://doi.org/10.33436/v35i4y202501