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OPEC Basket Monthly Crude Oil Price Forecasting: Comparative Study Between Prophet Facebook, NNAR, FTS Models

Author

Listed:
  • Abdelmounaim Hadjira

    (University of Mustapha Ben Boulaid-Batna 2)

  • Hicham Salhi

    (University of Mustapha Ben Boulaid-Batna 2)

  • Lyes Choubar

    (Mohamed Boudiaf University)

Abstract

Forecasting oil prices is a crucial and formidable task due to its far-reaching implications on both economic and non-economic factors. The influence of various factors such as economic growth, political developments, and psychological sentiments adds significant uncertainty to oil price forecasts. Despite extensive research, there remains a lack of consensus among scholars regarding the most effective techniques and models for predicting oil prices. Therefore, it is imperative to develop forecasting methods that offer higher accuracy and lower error rates to address this challenge effectively. In this study, the OPEC basket price data from 01–2003 to 06–2022 was used to forecast oil prices using three forecasting methodologies: the Neural Network Autoregressive (NNAR) model (Soft-computing model), Fuzzy Time Series (FTS) (qualitative models), and the Prophet by Facebook model. The performance of the models in modeling and forecasting was evaluated by: Mean Error (ME), Root Mean Square Error (RMSE), mean absolute error (MAE), Mean Absolute Percentage Error (MAPE). The results of the study showed that the best model according to the previous criteria is Singh's model with: $$ME=0.268, MAE=2.801, RMSE=3.347, MAPE=4.708.$$ M E = 0.268 , M A E = 2.801 , R M S E = 3.347 , M A P E = 4.708 . the study contributes valuable insights to the field of oil price forecasting, paving the way for the adoption and implementation of Fuzzy Time Series models, particularly the Singh model, as indispensable tools in understanding and anticipating the dynamic behavior of oil markets.

Suggested Citation

  • Abdelmounaim Hadjira & Hicham Salhi & Lyes Choubar, 2025. "OPEC Basket Monthly Crude Oil Price Forecasting: Comparative Study Between Prophet Facebook, NNAR, FTS Models," Computational Economics, Springer;Society for Computational Economics, vol. 66(2), pages 1647-1669, August.
    • Handle: RePEc:kap:compec:v:66:y:2025:i:2:d:10.1007_s10614-024-10762-7
    DOI: 10.1007/s10614-024-10762-7
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    References listed on IDEAS

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    1. Park, Jungwook & Ratti, Ronald A., 2008. "Oil price shocks and stock markets in the U.S. and 13 European countries," Energy Economics, Elsevier, vol. 30(5), pages 2587-2608, September.
    2. Abdollahi, Hooman, 2020. "A novel hybrid model for forecasting crude oil price based on time series decomposition," Applied Energy, Elsevier, vol. 267(C).
    3. Sean J. Taylor & Benjamin Letham, 2018. "Forecasting at Scale," The American Statistician, Taylor & Francis Journals, vol. 72(1), pages 37-45, January.
    4. Chellai, Fatih, 2022. "Forecasting using Fuzzy Time Series," MPRA Paper 113848, University Library of Munich, Germany.
    5. James D. Hamilton, 2009. "Understanding Crude Oil Prices," The Energy Journal, , vol. 30(2), pages 179-206, April.
    6. Esben Høg & Leonidas Tsiaras, 2011. "Density forecasts of crude‐oil prices using option‐implied and ARCH‐type models," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 31(8), pages 727-754, August.
    7. Zhang, Jin-Liang & Zhang, Yue-Jun & Zhang, Lu, 2015. "A novel hybrid method for crude oil price forecasting," Energy Economics, Elsevier, vol. 49(C), pages 649-659.
    8. He, Kaijian & Yu, Lean & Lai, Kin Keung, 2012. "Crude oil price analysis and forecasting using wavelet decomposed ensemble model," Energy, Elsevier, vol. 46(1), pages 564-574.
    9. Kaufmann, Robert K., 1995. "A model of the world oil market for project LINK Integrating economics, geology and politics," Economic Modelling, Elsevier, vol. 12(2), pages 165-178, April.
    10. Lama, A. & Jha, G.K. & Paul, R.K. & Gurung, B., 2015. "Modelling and Forecasting of Price Volatility: An Application of GARCH and EGARCH Models," Agricultural Economics Research Review, Agricultural Economics Research Association (India), vol. 28(01).
    11. Cheong, Chin Wen, 2009. "Modeling and forecasting crude oil markets using ARCH-type models," Energy Policy, Elsevier, vol. 37(6), pages 2346-2355, June.
    12. Chatziantoniou, Ioannis & Filippidis, Michail & Filis, George & Gabauer, David, 2021. "A closer look into the global determinants of oil price volatility," Energy Economics, Elsevier, vol. 95(C).
    13. Singh, S.R., 2008. "A computational method of forecasting based on fuzzy time series," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 79(3), pages 539-554.
    14. Lin, Yu & Xiao, Yang & Li, Fuxing, 2020. "Forecasting crude oil price volatility via a HM-EGARCH model," Energy Economics, Elsevier, vol. 87(C).
    15. Zhang, Yue-Jun & Yao, Ting & He, Ling-Yun & Ripple, Ronald, 2019. "Volatility forecasting of crude oil market: Can the regime switching GARCH model beat the single-regime GARCH models?," International Review of Economics & Finance, Elsevier, vol. 59(C), pages 302-317.
    16. Safari, Ali & Davallou, Maryam, 2018. "Oil price forecasting using a hybrid model," Energy, Elsevier, vol. 148(C), pages 49-58.
    17. repec:aen:journl:2009v30-02-a09 is not listed on IDEAS
    18. Mostafa, Mohamed M. & El-Masry, Ahmed A., 2016. "Oil price forecasting using gene expression programming and artificial neural networks," Economic Modelling, Elsevier, vol. 54(C), pages 40-53.
    19. Sepehr Ramyar & Farhad Kianfar, 2019. "Forecasting Crude Oil Prices: A Comparison Between Artificial Neural Networks and Vector Autoregressive Models," Computational Economics, Springer;Society for Computational Economics, vol. 53(2), pages 743-761, February.
    20. Movagharnejad, Kamyar & Mehdizadeh, Bahman & Banihashemi, Morteza & Kordkheili, Masoud Sheikhi, 2011. "Forecasting the differences between various commercial oil prices in the Persian Gulf region by neural network," Energy, Elsevier, vol. 36(7), pages 3979-3984.
    21. Lu, Quanying & Li, Yuze & Chai, Jian & Wang, Shouyang, 2020. "Crude oil price analysis and forecasting: A perspective of “new triangle”," Energy Economics, Elsevier, vol. 87(C).
    22. Morana, Claudio, 2001. "A semiparametric approach to short-term oil price forecasting," Energy Economics, Elsevier, vol. 23(3), pages 325-338, May.
    23. Rubaszek, Michał, 2021. "Forecasting crude oil prices with DSGE models," International Journal of Forecasting, Elsevier, vol. 37(2), pages 531-546.
    24. Reboredo, Juan C. & Rivera-Castro, Miguel A., 2014. "Wavelet-based evidence of the impact of oil prices on stock returns," International Review of Economics & Finance, Elsevier, vol. 29(C), pages 145-176.
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