Does Deep Learning Improve Forecast Accuracy of Crude Oil Prices? Evidence from a Neural Network Approach

Altug Aydemir and Mert Gokcu

CBT Research Notes in Economics from Research and Monetary Policy Department, Central Bank of the Republic of Turkey

Abstract: [EN] In recent years, machine learning-based techniques have gained prominence in forecasting crude oil prices due to their ability effectively handle the highly volatile and nonlinear nature of oil prices. The primary objective of this paper is to forecast monthly oil prices with the highest level of precision and accuracy possible. To do this, we propose a deepened and high-parametrized version of the deep neural network model framework that integrates widely adopted algorithms and a variety of datasets. Additionally, our approach involves the optimal architecture for deep neural networks used in oil price forecasting and offers forecasts that are repeatable and consistent. All the evaluation metrics values indicate that the proposed model achieves superior forecasting performance compared to some simple conventional statistical models. [TR] Son zamanlarda, makine ogrenimi tabanli yontemler, petrol fiyatlarinin son derece oynak ve dogrusal olmayan dogasi ile etkin bir sekilde basa cikma yetenekleri sayesinde ham petrol fiyatlarini tahmin etmede onem kazanmistir. Bu calismanin temel amaci, aylik bazda petrol fiyatlarini mumkun olan en yuksek hassasiyet ve dogrulukla tahmin etmektir. Bunu yapmak icin, ham petrol fiyat tahmini icin iyi bilinen algoritmalari ve cesitli veri kumelerini kullanan derin sinir agi modeli cercevesinin derinlestirilmis ve yuksek parametreli bir versiyonunu oneriyoruz. Ayrica, yaklasimimiz petrol fiyat tahmininde kullanilan derin sinir aglari icin en uygun mimariyi icermekte ve tekrarlanabilir ve tutarli tahminler sunmaktadir. Tum degerlendirme metrik degerleri, onerilen modelimizin geleneksel yontemlere kiyasla tahmin performansinda onemli bir iyilesmeye sahip oldugunu gostermektedir.

Date: 2025
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