A novel decompose-ensemble methodology with AIC-ANN approach for crude oil forecasting
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DOI: 10.1016/j.energy.2018.04.133
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- Ghassan, Hassan Belkacem & AlHajhoj, Hassan Rafdan, 2016.
"Long run dynamic volatilities between OPEC and non-OPEC crude oil prices,"
Applied Energy, Elsevier, vol. 169(C), pages 384-394.
- Ghassan, Hassan B. & Alhajhoj, Hassan R., 2015. "Long Run Dynamic Volatilities between OPEC and non-OPEC Crude Oil Prices," MPRA Paper 69962, University Library of Munich, Germany, revised 15 Jan 2016.
- Yu, Lean & Wang, Zishu & Tang, Ling, 2015. "A decomposition–ensemble model with data-characteristic-driven reconstruction for crude oil price forecasting," Applied Energy, Elsevier, vol. 156(C), pages 251-267.
- Zhang, Xun & Lai, K.K. & Wang, Shou-Yang, 2008. "A new approach for crude oil price analysis based on Empirical Mode Decomposition," Energy Economics, Elsevier, vol. 30(3), pages 905-918, May.
- Murat, Atilim & Tokat, Ekin, 2009. "Forecasting oil price movements with crack spread futures," Energy Economics, Elsevier, vol. 31(1), pages 85-90, January.
- Christiane Baumeister & Lutz Kilian, 2015.
"Forecasting the Real Price of Oil in a Changing World: A Forecast Combination Approach,"
Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 33(3), pages 338-351, July.
- Christiane Baumeister & Lutz Kilian, 2013. "Forecasting the Real Price of Oil in a Changing World: A Forecast Combination Approach," Staff Working Papers 13-28, Bank of Canada.
- Kilian, Lutz & Baumeister, Christiane, 2013. "Forecasting the Real Price of Oil in a Changing World: A Forecast Combination Approach," CEPR Discussion Papers 9569, C.E.P.R. Discussion Papers.
- Baumeister, Christiane & Kilian, Lutz, 2013. "Forecasting the real price of oil in a changing world: A forecast combination approach," CFS Working Paper Series 2013/11, Center for Financial Studies (CFS).
- Li, Ziran & Sun, Jiajing & Wang, Shouyang, 2013. "An information diffusion-based model of oil futures price," Energy Economics, Elsevier, vol. 36(C), pages 518-525.
- Baumeister, Christiane & Guérin, Pierre & Kilian, Lutz, 2015.
"Do high-frequency financial data help forecast oil prices? The MIDAS touch at work,"
International Journal of Forecasting, Elsevier, vol. 31(2), pages 238-252.
- Kilian, Lutz & Baumeister, Christiane, 2013. "Do High-Frequency Financial Data Help Forecast Oil Prices? The MIDAS Touch at Work," CEPR Discussion Papers 9768, C.E.P.R. Discussion Papers.
- Christiane Baumeister & Pierre Guérin & Lutz Kilian, 2014. "Do High-Frequency Financial Data Help Forecast Oil Prices? The MIDAS Touch at Work," Staff Working Papers 14-11, Bank of Canada.
- Baumeister, Christiane & Guérin, Pierre & Kilian, Lutz, 2013. "Do high-frequency financial data help forecast oil prices? The MIDAS touch at work," CFS Working Paper Series 2013/22, Center for Financial Studies (CFS).
- Yan-Fang Sang, 2013. "Improved Wavelet Modeling Framework for Hydrologic Time Series Forecasting," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 27(8), pages 2807-2821, June.
- 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.
- Ling Tang & Wei Dai & Lean Yu & Shouyang Wang, 2015. "A Novel CEEMD-Based EELM Ensemble Learning Paradigm for Crude Oil Price Forecasting," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 14(01), pages 141-169.
- He, Kaijian & Yu, Lean & Tang, Ling, 2015. "Electricity price forecasting with a BED (Bivariate EMD Denoising) methodology," Energy, Elsevier, vol. 91(C), pages 601-609.
- Chiroma, Haruna & Abdulkareem, Sameem & Herawan, Tutut, 2015. "Evolutionary Neural Network model for West Texas Intermediate crude oil price prediction," Applied Energy, Elsevier, vol. 142(C), pages 266-273.
- Aloui, Riadh & Gupta, Rangan & Miller, Stephen M., 2016.
"Uncertainty and crude oil returns,"
Energy Economics, Elsevier, vol. 55(C), pages 92-100.
- Riadh Aloui & Rangan Gupta & Stephen M. Miller, 2015. "Uncertainty and Crude Oil Returns," Working Papers 201503, University of Pretoria, Department of Economics.
- Riadh Aloui & Rangan Gupta & Stephen M. Miller, 2015. "Uncertainty and crude oil returns," Working papers 2015-03, University of Connecticut, Department of Economics.
- Tang, Ling & Yu, Lean & Wang, Shuai & Li, Jianping & Wang, Shouyang, 2012. "A novel hybrid ensemble learning paradigm for nuclear energy consumption forecasting," Applied Energy, Elsevier, vol. 93(C), pages 432-443.
- Abramson, Bruce & Finizza, Anthony, 1991. "Using belief networks to forecast oil prices," International Journal of Forecasting, Elsevier, vol. 7(3), pages 299-315, November.
- Efimova, Olga & Serletis, Apostolos, 2014.
"Energy markets volatility modelling using GARCH,"
Energy Economics, Elsevier, vol. 43(C), pages 264-273.
- Olga Efimova & Apostolos Serletis, "undated". "Energy Markets Volatility Modelling using GARCH," Working Papers 2014-39, Department of Economics, University of Calgary, revised 24 Feb 2014.
- Aloui, Chaker & Mabrouk, Samir, 2010. "Value-at-risk estimations of energy commodities via long-memory, asymmetry and fat-tailed GARCH models," Energy Policy, Elsevier, vol. 38(5), pages 2326-2339, May.
- Wang, Deyun & Luo, Hongyuan & Grunder, Olivier & Lin, Yanbing & Guo, Haixiang, 2017. "Multi-step ahead electricity price forecasting using a hybrid model based on two-layer decomposition technique and BP neural network optimized by firefly algorithm," Applied Energy, Elsevier, vol. 190(C), pages 390-407.
- Lanza, Alessandro & Manera, Matteo & Giovannini, Massimo, 2005. "Modeling and forecasting cointegrated relationships among heavy oil and product prices," Energy Economics, Elsevier, vol. 27(6), pages 831-848, November.
- Yuan, Chunming, 2011.
"Forecasting exchange rates: The multi-state Markov-switching model with smoothing,"
International Review of Economics & Finance, Elsevier, vol. 20(2), pages 342-362, April.
- Chunming Yuan, 2008. "Forecasting Exchange Rates: The Multi-State Markov-Switching Model with Smoothing," UMBC Economics Department Working Papers 09-115, UMBC Department of Economics, revised 01 Nov 2009.
- Zhu, Bangzhu & Han, Dong & Wang, Ping & Wu, Zhanchi & Zhang, Tao & Wei, Yi-Ming, 2017. "Forecasting carbon price using empirical mode decomposition and evolutionary least squares support vector regression," Applied Energy, Elsevier, vol. 191(C), pages 521-530.
- Clark, Todd E. & McCracken, Michael W., 2001.
"Tests of equal forecast accuracy and encompassing for nested models,"
Journal of Econometrics, Elsevier, vol. 105(1), pages 85-110, November.
- Todd E. Clark & Michael W. McCracken, 1999. "Tests of equal forecast accuracy and encompassing for nested models," Research Working Paper 99-11, Federal Reserve Bank of Kansas City.
- Todd E. Clark & Michael W. McCracken, 2000. "Tests of Equal Forecast Accuracy and Encompassing for Nested Models," Econometric Society World Congress 2000 Contributed Papers 0319, Econometric Society.
- Todd E. Clark & Michael McCracken, 1999. "Tests of Equal Forecast Accuracy and Encompassing for Nested Models," Computing in Economics and Finance 1999 1241, Society for Computational Economics.
- Yu, Lean & Zhao, Yang & Tang, Ling, 2014. "A compressed sensing based AI learning paradigm for crude oil price forecasting," Energy Economics, Elsevier, vol. 46(C), pages 236-245.
- Christiane Baumeister & Lutz Kilian, 2014.
"Real-Time Analysis of Oil Price Risks Using Forecast Scenarios,"
IMF Economic Review, Palgrave Macmillan;International Monetary Fund, vol. 62(1), pages 119-145, April.
- Kilian, Lutz & Baumeister, Christiane, 2011. "Real-Time Analysis of Oil Price Risks Using Forecast Scenarios," CEPR Discussion Papers 8698, C.E.P.R. Discussion Papers.
- Christiane Baumeister & Lutz Kilian, 2012. "Real-Time Analysis of Oil Price Risks Using Forecast Scenarios," Staff Working Papers 12-1, Bank of Canada.
- Wang, Yudong & Liu, Li & Diao, Xundi & Wu, Chongfeng, 2015. "Forecasting the real prices of crude oil under economic and statistical constraints," Energy Economics, Elsevier, vol. 51(C), pages 599-608.
- Christiane Baumeister & Lutz Kilian, 2014.
"What Central Bankers Need To Know About Forecasting Oil Prices,"
International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 55, pages 869-889, August.
- Baumeister, Christiane & Kilian, Lutz, 2012. "What Central Bankers Need to Know about Forecasting Oil Prices," CEPR Discussion Papers 9118, C.E.P.R. Discussion Papers.
- Christiane Baumeister & Lutz Kilian, 2013. "What Central Bankers Need to Know about Forecasting Oil Prices," Staff Working Papers 13-15, Bank of Canada.
- Xiong, Tao & Bao, Yukun & Hu, Zhongyi, 2013. "Beyond one-step-ahead forecasting: Evaluation of alternative multi-step-ahead forecasting models for crude oil prices," Energy Economics, Elsevier, vol. 40(C), pages 405-415.
- Diebold, Francis X & Mariano, Roberto S, 2002.
"Comparing Predictive Accuracy,"
Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 134-144, January.
- Diebold, Francis X & Mariano, Roberto S, 1995. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 13(3), pages 253-263, July.
- Francis X. Diebold & Roberto S. Mariano, 1994. "Comparing Predictive Accuracy," NBER Technical Working Papers 0169, National Bureau of Economic Research, Inc.
- Yu, Lean & Wang, Shouyang & Lai, Kin Keung, 2008. "Forecasting crude oil price with an EMD-based neural network ensemble learning paradigm," Energy Economics, Elsevier, vol. 30(5), pages 2623-2635, September.
- 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.
- Serletis, Apostolos & Xu, Libo, 2016.
"Volatility and a century of energy markets dynamics,"
Energy Economics, Elsevier, vol. 55(C), pages 1-9.
- Apostolos Serletis & Libo Xu, "undated". "Volatility and a Century of Energy Markets Dynamics," Working Papers 2016-29, Department of Economics, University of Calgary, revised 28 Jan 2016.
- Siddhivinayak Kulkarni & Imad Haidar, 2009. "Forecasting Model for Crude Oil Price Using Artificial Neural Networks and Commodity Futures Prices," Papers 0906.4838, arXiv.org.
- Shambora, William E. & Rossiter, Rosemary, 2007. "Are there exploitable inefficiencies in the futures market for oil?," Energy Economics, Elsevier, vol. 29(1), pages 18-27, January.
- 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.
- Harvey, David & Leybourne, Stephen & Newbold, Paul, 1997. "Testing the equality of prediction mean squared errors," International Journal of Forecasting, Elsevier, vol. 13(2), pages 281-291, June.
- Kyongwook Choi & Shawkat Hammoudeh, 2009. "Long Memory in Oil and Refined Products Markets," The Energy Journal, International Association for Energy Economics, vol. 0(Number 2), pages 97-116.
- 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.
- Yang, Zhang & Ce, Li & Lian, Li, 2017. "Electricity price forecasting by a hybrid model, combining wavelet transform, ARMA and kernel-based extreme learning machine methods," Applied Energy, Elsevier, vol. 190(C), pages 291-305.
- Morana, Claudio, 2001. "A semiparametric approach to short-term oil price forecasting," Energy Economics, Elsevier, vol. 23(3), pages 325-338, May.
- Hou, Aijun & Suardi, Sandy, 2012. "A nonparametric GARCH model of crude oil price return volatility," Energy Economics, Elsevier, vol. 34(2), pages 618-626.
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Keywords
Crude oil price prediction; Ensemble empirical mode decomposition; Akaike's information criterion; Hybrid model; Predicting accuracy; Stability;All these keywords.
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