Multi-Step Crude Oil Price Prediction Based on LSTM Approach Tuned by Salp Swarm Algorithm with Disputation Operator

Luka Jovanovic, Dejan Jovanovic, Nebojsa Bacanin (), Ana Jovancai Stakic, Milos Antonijevic, Hesham Magd, Ravi Thirumalaisamy and Miodrag Zivkovic
Additional contact information
Luka Jovanovic: Faculty of Informatics and Computing, Singidunum University, Danijelova 32, 11010 Belgrade, Serbia
Dejan Jovanovic: College of Academic Studies “Dositej”, Bulevar Vojvode Putnika 7, 11000 Belgrade, Serbia
Nebojsa Bacanin: Faculty of Informatics and Computing, Singidunum University, Danijelova 32, 11010 Belgrade, Serbia
Ana Jovancai Stakic: Faculty of Informatics and Computing, Singidunum University, Danijelova 32, 11010 Belgrade, Serbia
Milos Antonijevic: Faculty of Informatics and Computing, Singidunum University, Danijelova 32, 11010 Belgrade, Serbia
Hesham Magd: Business and Economics, Modern College of Business and Science, P.O. Box 100, Al-Khuwaur, Muscat 133, Oman
Ravi Thirumalaisamy: Business and Economics, Modern College of Business and Science, P.O. Box 100, Al-Khuwaur, Muscat 133, Oman
Miodrag Zivkovic: Faculty of Informatics and Computing, Singidunum University, Danijelova 32, 11010 Belgrade, Serbia

Sustainability, 2022, vol. 14, issue 21, 1-29

Abstract: The economic model derived from the supply and demand of crude oil prices is a significant component that measures economic development and sustainability. Therefore, it is essential to mitigate crude oil price volatility risks by establishing models that will effectively predict prices. A promising approach is the application of long short-term memory artificial neural networks for time-series forecasting. However, their ability to tackle complex time series is limited. Therefore, a decomposition-forecasting approach is taken. Furthermore, machine learning model accuracy is highly dependent on hyper-parameter settings. Therefore, in this paper, a modified version of the salp swarm algorithm is tasked with determining satisfying parameters of the long short-term memory model to improve the performance and accuracy of the prediction algorithm. The proposed approach is validated on real-world West Texas Intermediate (WTI) crude oil price data throughout two types of experiments, one with the original time series and one with the decomposed series after applying variation mode decomposition. In both cases, models were adjusted to conduct one, three, and five-steps ahead predictions. According to the findings of comparative analysis with contemporary metaheuristics, it was concluded that the proposed hybrid approach is promising for crude oil price forecasting, outscoring all competitors.

Keywords: optimization; crude oil price; prediction; swarm intelligence; salp swarm algorithm; VMD; LSTM; machine learning tuning (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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