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Forecasting Model for Crude Oil Price Using Artificial Neural Networks and Commodity Futures Prices

Citations

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Cited by:

  1. Jiang Wu & Yu Chen & Tengfei Zhou & Taiyong Li, 2019. "An Adaptive Hybrid Learning Paradigm Integrating CEEMD, ARIMA and SBL for Crude Oil Price Forecasting," Energies, MDPI, vol. 12(7), pages 1-23, April.
  2. Jiang Wu & Feng Miu & Taiyong Li, 2020. "Daily Crude Oil Price Forecasting Based on Improved CEEMDAN, SCA, and RVFL: A Case Study in WTI Oil Market," Energies, MDPI, vol. 13(7), pages 1-20, April.
  3. Ramesh Bollapragada & Akash Mankude & V. Udayabhanu, 2021. "Forecasting the price of crude oil," DECISION: Official Journal of the Indian Institute of Management Calcutta, Springer;Indian Institute of Management Calcutta, vol. 48(2), pages 207-231, June.
  4. Manel Hamdi & Chaker Aloui & Santosh kumar Nanda, 2016. "Comparing Functional Link Artificial Neural Network And Multilayer Feedforward Neural Network Model To Forecast Crude Oil Prices," Economics Bulletin, AccessEcon, vol. 36(4), pages 2430-2442.
  5. Safari, Ali & Davallou, Maryam, 2018. "Oil price forecasting using a hybrid model," Energy, Elsevier, vol. 148(C), pages 49-58.
  6. Gustavo Carvalho Santos & Flavio Barboza & Antônio Cláudio Paschoarelli Veiga & Mateus Ferreira Silva, 2021. "Forecasting Brazilian Ethanol Spot Prices Using LSTM," Energies, MDPI, vol. 14(23), pages 1-15, November.
  7. Ding, Yishan, 2018. "A novel decompose-ensemble methodology with AIC-ANN approach for crude oil forecasting," Energy, Elsevier, vol. 154(C), pages 328-336.
  8. Butler, Sunil & Kokoszka, Piotr & Miao, Hong & Shang, Han Lin, 2021. "Neural network prediction of crude oil futures using B-splines," Energy Economics, Elsevier, vol. 94(C).
  9. Drachal, Krzysztof, 2016. "Forecasting spot oil price in a dynamic model averaging framework — Have the determinants changed over time?," Energy Economics, Elsevier, vol. 60(C), pages 35-46.
  10. Lean Yu & Yueming Ma, 2021. "A Data-Trait-Driven Rolling Decomposition-Ensemble Model for Gasoline Consumption Forecasting," Energies, MDPI, vol. 14(15), pages 1-26, July.
  11. Christian Gourieroux & Joann Jasiak & Michelle Tong, 2021. "Convolution‐based filtering and forecasting: An application to WTI crude oil prices," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(7), pages 1230-1244, November.
  12. Zuzanna Karolak, 2021. "Energy prices forecasting using nonlinear univariate models," Bank i Kredyt, Narodowy Bank Polski, vol. 52(6), pages 577-598.
  13. Krzysztof Drachal, 2018. "Determining Time-Varying Drivers of Spot Oil Price in a Dynamic Model Averaging Framework," Energies, MDPI, vol. 11(5), pages 1-24, May.
  14. Jammazi, Rania & Aloui, Chaker, 2012. "Crude oil price forecasting: Experimental evidence from wavelet decomposition and neural network modeling," Energy Economics, Elsevier, vol. 34(3), pages 828-841.
  15. Adamu Abubakar & Haruna Chiroma & Abdullah Khan & Mukhtar Fatihu Hamza & Ali Baba Dauda & Mahmood Nadeem & Shah Asadullah & Jaafar Zubairu Maitama & Tutut Herawan, 2016. "Utilizing Modular Neural Network for Prediction of Possible Emergencies Locations within Point of Interest of Hajj Pilgrimage," Modern Applied Science, Canadian Center of Science and Education, vol. 10(2), pages 1-34, February.
  16. Jammazi, Rania, 2012. "Oil shock transmission to stock market returns: Wavelet-multivariate Markov switching GARCH approach," Energy, Elsevier, vol. 37(1), pages 430-454.
  17. Mona Shazly & Alice Lou, 2016. "Comparing the Forecasting Performance of Futures Oil Prices with Genetically Evolved Neural Networks," International Advances in Economic Research, Springer;International Atlantic Economic Society, vol. 22(4), pages 361-376, November.
  18. Sudhi SHARMA & Miklesh YADAV, 2020. "Analyzing the robustness of ARIMA and neural networks as a predictive model of crude oil prices," Theoretical and Applied Economics, Asociatia Generala a Economistilor din Romania / Editura Economica, vol. 0(2(623), S), pages 289-300, Summer.
  19. Keles, Dogan & Scelle, Jonathan & Paraschiv, Florentina & Fichtner, Wolf, 2016. "Extended forecast methods for day-ahead electricity spot prices applying artificial neural networks," Applied Energy, Elsevier, vol. 162(C), pages 218-230.
  20. Madadkhani, Shiva & Ikonnikova, Svetlana, 2024. "Toward high-resolution projection of electricity prices: A machine learning approach to quantifying the effects of high fuel and CO2 prices," Energy Economics, Elsevier, vol. 129(C).
  21. Kakade, Kshitij & Jain, Ishan & Mishra, Aswini Kumar, 2022. "Value-at-Risk forecasting: A hybrid ensemble learning GARCH-LSTM based approach," Resources Policy, Elsevier, vol. 78(C).
  22. Manuel Monge & Ana Lazcano, 2022. "Commodity Prices after COVID-19: Persistence and Time Trends," Risks, MDPI, vol. 10(6), pages 1-20, June.
  23. Lang, Korbinian & Auer, Benjamin R., 2020. "The economic and financial properties of crude oil: A review," The North American Journal of Economics and Finance, Elsevier, vol. 52(C).
  24. Singh, Sriramjee, 2020. "Predicting CBOT Corn Futures Prices by applying ML methods on Weather Data," 2020 Annual Meeting, July 26-28, Kansas City, Missouri 304595, Agricultural and Applied Economics Association.
  25. Manel Hamdi & Chaker Aloui, 2015. "Forecasting Crude Oil Price Using Artificial Neural Networks: A Literature Survey," Economics Bulletin, AccessEcon, vol. 35(2), pages 1339-1359.
  26. Süleyman Bilgin Kılıç & Semin Paksoy & Tolga Genç, 2014. "Forecasting the Direction of BIST 100 Returns with Artificial Neural Network Models," International Journal of Finance, Insurance and Risk Management, International Journal of Finance, Insurance and Risk Management, vol. 4(3), pages 759-759.
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