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Cited by 380

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A deep learning ensemble approach for crude oil price forecasting

Zhao, Yang, Li, Jianping, Yu, Lean·2017·Energy Economics
DOI 10.1016/j.eneco.2017.05.023
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Research works printed but not linked: 33

  1. Alquist, 2013, Chapter 8 - forecasting the price of oil, 427, 10.1016/B978-0-444-53683-9.00008-6 · DOI · Paper page
  2. Baumeister, 2016, Forty years of oil price fluctuations: why the price of oil may still surprise us, J. Econ. Perspect., 30, 139, 10.1257/jep.30.1.139 · DOI · Paper page
  3. Bengio, 2009, Learning deep architectures for AI. Found, Trends Mach. Learn., 2, 1, 10.1561/2200000006 · DOI · Paper page
  4. Chiroma, 2015, Evolutionary neural network model for West Texas intermediate crude oil price prediction, Appl. Energy, 142, 266, 10.1016/j.apenergy.2014.12.045 · doi:10.1016/j.apenergy.2014.12.045
  5. Diebold, 1995, Comparing predictive accuracy, J. Bus. Econ. Stat., 13, 253, 10.1080/07350015.1995.10524599 · DOI · Paper page
  6. Drachal, 2016, Forecasting spot oil price in a dynamic model averaging framework — have the determinants changed over time?, Energy Econ., 60, 35, 10.1016/j.eneco.2016.09.020 · DOI
  7. Ghaffari, 2009, A novel algorithm for prediction of crude oil price variation based on soft computing, Energy Econ., 31, 531, 10.1016/j.eneco.2009.01.006 · doi:10.1016/j.eneco.2009.01.006
  8. Godarzi, 2014, Predicting oil price movements: a dynamic artificial neural network approach, Energ Policy, 68, 371, 10.1016/j.enpol.2013.12.049 · DOI
  9. Grushka-Cockayne, 2017, Ensembles of overfit and overconfident forecasts, Manag. Sci., 63, 1110, 10.1287/mnsc.2015.2389 · DOI
  10. Harvey, 1998, Tests for forecast encompassing, J. Bus. Econ. Stat., 16, 254, 10.1080/07350015.1998.10524759 · DOI · Paper page
  11. Hinton, 2006, Reducing the dimensionality of data with neural networks, Science, 313, 504, 10.1126/science.1127647 · DOI · Paper page
  12. Jammazi, 2012, Crude oil price forecasting: experimental evidence from wavelet decomposition and neural network modeling, Energy Econ., 34, 828, 10.1016/j.eneco.2011.07.018 · DOI · Paper page
  13. Kaboudan, 2001, Compumetric forecasting of crude oil prices, IEEE C Evol. Comput., 283
  14. Kim, 2010, Ensemble with neural networks for bankruptcy prediction, Expert Syst. Appl., 37, 3373, 10.1016/j.eswa.2009.10.012 · DOI · Paper page
  15. Koop, 2012, Forecasting inflation using dynamic model averaging*, Int. Econ. Rev., 53, 867, 10.1111/j.1468-2354.2012.00704.x · DOI · Paper page
  16. Kourentzes, 2014, Neural network ensemble operators for time series forecasting, Expert Syst. Appl., 41, 4235, 10.1016/j.eswa.2013.12.011 · DOI · Paper page
  17. Moshiri, 2006, Forecasting nonlinear crude oil futures prices, Energy J., 27, 81, 10.5547/ISSN0195-6574-EJ-Vol27-No4-4 · doi:10.5547/issn0195-6574-ej-vol27-no4-4
  18. Mostafa, 2016, Oil price forecasting using gene expression programming and artificial neural networks, Econ. Model., 54, 40, 10.1016/j.econmod.2015.12.014 · DOI
  19. Naifar, 2013, Nonlinear analysis among crude oil prices, stock markets' return and macroeconomic variables, Int. Rev. Econ. Financ., 27, 416, 10.1016/j.iref.2013.01.001 · DOI · Paper page
  20. Naser, 2016, Estimating and forecasting the real prices of crude oil: a data rich model using a dynamic model averaging (DMA) approach, Energy Econ., 56, 75, 10.1016/j.eneco.2016.02.017 · DOI
  21. Peter, 1988, Testing for a unit root in time series regression, Biometrika, 75, 335, 10.1093/biomet/75.2.335 · DOI · Paper page
  22. Shin, 2013, Prediction of movement direction in crude oil prices based on semi-supervised learning, Decis. Support. Syst., 55, 348, 10.1016/j.dss.2012.11.009 · DOI
  23. Vincent, 2010, Stacked denoising autoencoders: learning useful representations in a deep network with a local denoising criterion, J. Mach. Learn. Res., 11, 3371
  24. Wang, 2016, Forecasting crude oil market volatility: a Markov switching multifractal volatility approach, Int. J. Forecast., 32, 1, 10.1016/j.ijforecast.2015.02.006 · DOI
  25. White, 2000, A reality check for data snooping, Econometrica, 68, 1097, 10.1111/1468-0262.00152 · DOI · Paper page
  26. Xiong, 2013, Beyond one-step-ahead forecasting: evaluation of alternative multi-step-ahead forecasting models for crude oil prices, Energy Econ., 40, 405, 10.1016/j.eneco.2013.07.028 · DOI · Paper page
  27. Ye, 2006, Forecasting short-run crude oil price using high- and low-inventory variables, Energ Policy, 34, 2736, 10.1016/j.enpol.2005.03.017 · doi:10.1016/j.enpol.2005.03.017
  28. Yu, 2008, Credit risk assessment with a multistage neural network ensemble learning approach, Expert Syst. Appl., 34, 1434, 10.1016/j.eswa.2007.01.009 · DOI · Paper page
  29. Yu, 2008, Forecasting crude oil price with an EMD-based neural network ensemble learning paradigm, Energy Econ., 30, 2623, 10.1016/j.eneco.2008.05.003 · DOI
  30. Yu, 2014, A compressed sensing based AI learning paradigm for crude oil price forecasting, Energy Econ., 46, 236, 10.1016/j.eneco.2014.09.019 · DOI
  31. Yu, 2016, Ensemble forecasting for complex time series using sparse representation and neural networks, J. Forecast.
  32. Zagaglia, 2010, Macroeconomic factors and oil futures prices: a data-rich model, Energy Econ., 32, 409, 10.1016/j.eneco.2009.11.003 · DOI
  33. Zhang, 2015, Interpreting the crude oil price movements: evidence from the Markov regime switching model, Appl. Energy, 143, 96, 10.1016/j.apenergy.2015.01.005 · doi:10.1016/j.apenergy.2015.01.005

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Metadata

Authors
Zhao, Y., Li, J., & Yu, L.
Venue
Energy Economics
Publisher
Elsevier BV
Published
2017-08
Volume
66
Pages
9-16
ISSN
0140-9883 (ISSN-L)
DOI
10.1016/j.eneco.2017.05.023
OpenAlex
W2622999711
Cited by
380
References
4
FWCI
29.99
Type
journal article
Language
en
Open access
closed
Licence
www.elsevier.com/tdm/userlicense/1.0/ (tdm)
Funders
National Natural Science Foundation of China (71425002, 71433001)

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CoChat. (2026). Citation check: A deep learning ensemble approach for crude oil price forecasting. https://app.cochat.ai/papers/10.1016/j.eneco.2017.05.023
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Zhao, Y., Li, J., & Yu, L. (2017). A deep learning ensemble approach for crude oil price forecasting. Energy Economics. https://doi.org/10.1016/j.eneco.2017.05.023

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