Predicting Crude Oil Future Price Using Traditional and Artificial Intelligence-Based Model: Comparative Analysis

Sanjeev Kadam, Anshul Agrawal (), Aryan Bajaj (), Rachit Agarwal (), Rameesha Kalra () and Jaymin Shah ()
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Sanjeev Kadam: Symbiosis Institute of Business Management Pune, Symbiosis International (Deemed University), Maharashtra, India
Anshul Agrawal: ��Jaypee Institute of Information Technology, Noida, India
Aryan Bajaj: ��GISMA School of Business, University of Applied Sciences, Potsdam, Germany
Rachit Agarwal: �University School of Business, Chandigarh University, Mohali, 140413, Punjab, India
Rameesha Kalra: �School of Business Management, CHRIST (Deemed to be University), Bangalore, India
Jaymin Shah: ��Amity Business School, Amity University Mumbai, Panvel, India

Journal of International Commerce, Economics and Policy (JICEP), 2023, vol. 14, issue 03, 1-15

Abstract: Crude oil is an imperative energy source for the global economy. The future value of crude oil is challenging to anticipate due to its nonstationarity in nature. The focus of this research is to appraise the explosive behavior of crude oil during 2007–2022, including the most recent influential crisis COVID-19 pandemic, to forecast its prices. The crude oil price forecasts by the traditional econometric ARIMA model were compared with modern Artificial Intelligence (AI)-based Long Short-Term Memory Networks (ALSTM). Root mean square error (RMSE) and mean average percent error (MAPE) values have been used to evaluate the accuracy of such approaches. The results showed that the ALSTM model performs better than the traditional econometric ARIMA forecast model while predicting crude oil opening price on the next working day. Crude oil investors can effectively use this as an intraday trading model and more accurately predict the next working day opening price.

Keywords: Artificial intelligence; ALSTM; ARIMA; crude oil; forecast; RNN-LSTM (search for similar items in EconPapers)
JEL-codes: C53 F47 Q47 (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1142/S179399332350014X

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