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Introducing the Synergy Based Approach for Forecasting the Crude Oil Prices with Traditional and Machine Learning Econometric Models

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  • Arslan Munir Turk
  • Saud Ahmed Khan
  • Muhammad Aamir
  • Zahanat Hussain

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  • Arslan Munir Turk & Saud Ahmed Khan & Muhammad Aamir & Zahanat Hussain, 2025. "Introducing the Synergy Based Approach for Forecasting the Crude Oil Prices with Traditional and Machine Learning Econometric Models," International Econometric Review (IER), Economic Research Association, vol. 17(2), pages 18-33, December.
    • Handle: RePEc:erh:journl:v:17:y:2025:i:2:p:18-33
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    2. Hyndman, Rob J. & Koehler, Anne B., 2006. "Another look at measures of forecast accuracy," International Journal of Forecasting, Elsevier, vol. 22(4), pages 679-688.
    3. Golyandina, Nina & Korobeynikov, Anton, 2014. "Basic Singular Spectrum Analysis and forecasting with R," Computational Statistics & Data Analysis, Elsevier, vol. 71(C), pages 934-954.
    4. Sendhil Mullainathan & Jann Spiess, 2017. "Machine Learning: An Applied Econometric Approach," Journal of Economic Perspectives, American Economic Association, vol. 31(2), pages 87-106, Spring.
    5. Spyros Makridakis & Evangelos Spiliotis & Vassilios Assimakopoulos, 2018. "Statistical and Machine Learning forecasting methods: Concerns and ways forward," PLOS ONE, Public Library of Science, vol. 13(3), pages 1-26, March.
    6. Panagiotelis, Anastasios & Gamakumara, Puwasala & Athanasopoulos, George & Hyndman, Rob J., 2023. "Probabilistic forecast reconciliation: Properties, evaluation and score optimisation," European Journal of Operational Research, Elsevier, vol. 306(2), pages 693-706.
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