WTI Crude Oil Price Prediction Using ARIMA and Neural Network Models: An Analysis for 2022
Keywords:
oil, economic forescasting, mathematical models, neural networks, market volatilityAbstract
DOI:
http://doi.org/10.5281/zenodo.15200039
This study compares the performance of ARIMA and Neural Network models in forecasting oil prices during 2022, a year marked by high volatility due to factors such as the war in Ukraine. A total of 252 daily data points were analyzed, including exogenous variables such as interest rates, supply, demand, and trade balance. Results show that Neural Networks outperformed the ARIMA (3, 0, 1) model in terms of accuracy, achieving a lower mean absolute error (2.018 vs. 2.34 USD) and a lower percentage error (2.66% vs. 3.21%). Moreover, the Neural Network model improved its performance over time, whereas ARIMA showed a decline in long-term forecasting accuracy. Although ARIMA proved more efficient in the short term and required less data, the Neural Network better captured the market's nonlinear dynamics, with a correlation coefficient of 19.56% compared to ARIMA’s 5.49%. The study concludes that Neural Networks provide greater robustness and accuracy in prolonged periods of uncertainty and recommends exploring hybrid models that combine the strengths of both approaches
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