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A hybrid grid-GA-based LSSVR learning paradigm for crude oil price forecasting

  • Predictive Analytics Using Machine Learning
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Abstract

In order to effectively model crude oil spot price with inherently high complexity, a hybrid learning paradigm integrating least squares support vector regression (LSSVR) with a hybrid optimization searching approach for the parameters selection in the LSSVR [consisting of grid method and genetic algorithm (GA)], i.e., a hybrid grid-GA-based LSSVR model, is proposed in this study. In the proposed hybrid learning paradigm, the grid method, a simple but efficient searching method, is first applied to roughly but rapidly determine the proper boundaries of the parameters in the LSSVR; then, the GA, an effective and powerful intelligent searching algorithm, is further implemented to select the most suitable parameters. For illustration and verification, the proposed learning paradigm is used to predict the crude oil spot prices of the West Texas Intermediate and the Brent markets. The empirical results demonstrate that the proposed hybrid grid-GA-based LSSVR learning paradigm can outperform its benchmarking models (including some popular forecasting techniques and similar LSSVRs with other parameter searching algorithms) in terms of both prediction accuracy and time-savings, indicating that it can be utilized as one effective forecasting tool for crude oil price with high volatility and irregularity.

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References

  1. He K, Yu L, Lai KK (2012) Crude oil price analysis and forecasting using wavelet decomposed ensemble model. Energy 46(1):564–574. doi:10.1016/j.energy.2012.07.055

    Article  Google Scholar 

  2. Behmiri NB, Manso JRP (2013) How crude oil consumption impacts on economic growth of Sub-Saharan Africa? Energy 54:74–83. doi:10.1016/j.energy.2013.02.052

    Article  Google Scholar 

  3. Narayan PK, Narayan S, Zheng X (2010) Gold and oil futures markets: are markets efficient? Appl Energy 87(10):3299–3303. doi:10.1016/j.apenergy.2010.03.020

    Article  Google Scholar 

  4. Cavalcanti T, Jalles JT (2013) Macroeconomic effects of oil price shocks in Brazil and in the United States. Appl Energy 104:475–486. doi:10.1016/j.apenergy.2012.10.039

    Article  Google Scholar 

  5. Jammazi R, Aloui C (2012) Crude oil price forecasting: experimental evidence from wavelet decomposition and neural network modeling. Energy Econ 34(3):828–841. doi:10.1016/j.eneco.2011.07.018

    Article  Google Scholar 

  6. Tang L, Yu L, Liu F, Xu W (2013) An integrated data characteristic testing scheme for complex time series data exploration. Int J Inf Technol Decis Mak 12(3):491–521. doi:10.1142/S0219622013500193

    Article  Google Scholar 

  7. Ji Q, Fan Y (2012) How does oil price volatility affect non-energy commodity markets? Appl Energy 89(1):273–280. doi:10.1016/j.apenergy.2011.07.038

    Article  Google Scholar 

  8. Logar I, van den Bergh JC (2013) The impact of peak oil on tourism in Spain: an input–output analysis of price, demand and economy-wide effects. Energy 54:155–166. doi:10.1016/j.energy.2013.01.072

    Article  Google Scholar 

  9. Zhang X, Yu L, Wang S, Lai KK (2009) Estimating the impact of extreme events on crude oil price: an EMD-based event analysis method. Energy Econ 31(5):768–778. doi:10.1016/j.eneco.2009.04.003

    Article  Google Scholar 

  10. Bao Y, Zhang X, Yu L, Lai KK, Wang S (2007, July) Hybridizing wavelet and least squares support vector machines for crude oil price forecasting. In: Proceedings of the 2nd international workshop on intelligent finance

  11. Yu L, Lai KK, Wang SY, He K (2007) Oil price forecasting with an EMD-based multiscale neural network learning paradigm. In: Shi Y, Albada GD, Dongarra J, Sloot PMA (eds) Computational science—ICCS 2007. Springer, Berlin, pp 925–932

    Chapter  Google Scholar 

  12. Wang S, Yu L, Tang L, Wang SY (2011) A novel seasonal decomposition based least squares support vector regression ensemble learning approach for hydropower consumption forecasting in China. Energy 36(11):6542–6554. doi:10.1016/j.energy.2011.09.010

    Article  Google Scholar 

  13. Tang L, Yu L, Wang S, Li JP, Wang SY (2012) A novel hybrid ensemble learning paradigm for nuclear energy consumption forecasting. Appl Energy 93:432–443. doi:10.1016/j.apenergy.2011.12.030

    Article  Google Scholar 

  14. Abdel-Aal RE, Al-Garni AZ (1997) Forecasting monthly electric energy consumption in eastern Saudi Arabia using univariate time-series analysis. Energy 22(11):1059–1069. doi:10.1016/S0360-5442(97)00032-7

    Article  Google Scholar 

  15. Tang L, Wang S, He K, Wang SY (2014) A novel mode-characteristic-based decomposition ensemble model for nuclear energy consumption forecasting. Ann Oper Res 2014:1–22. doi:10.1007/s10479-014-1595-5

    MathSciNet  MATH  Google Scholar 

  16. Frey G, Manera M, Markandya A, Scarpa E (2009) Econometric models for oil price forecasting: a critical survey. In CESifo Forum. Ifo Inst Econ Res 1:29–44. doi:10.1007/s10479-014-1595-5

    Google Scholar 

  17. Yu L, Wang SY, Lai KK (2008) Forecasting crude oil price with an EMD-based neural network ensemble learning paradigm. Energy Econ 30(5):2623–2635. doi:10.1016/j.eneco.2008.05.003

    Article  MATH  Google Scholar 

  18. Zhou ZB, Dong XC (2012) Analysis about the seasonality of China’s crude oil import based on X-12-ARIMA. Energy 42(1):281–288. doi:10.1016/j.energy.2012.03.058

    Article  MathSciNet  Google Scholar 

  19. Nomikos N, Andriosopoulos K (2012) Modelling energy spot prices: empirical evidence from NYMEX. Energy Econ 34(4):1153–1169. doi:10.1016/j.eneco.2011.10.001

    Article  Google Scholar 

  20. Murat A, Tokat E (2009) Forecasting oil price movements with crack spread futures. Energy Econ 31(1):85–90. doi:10.1016/j.eneco.2008.07.008

    Article  Google Scholar 

  21. Lanza A, Manera M, Giovannini M (2005) Modeling and forecasting cointegrated relationships among heavy oil and product prices. Energy Econ 27(6):831–848. doi:10.1016/j.eneco.2005.07.001

    Article  Google Scholar 

  22. Zhang GP, Patuwo BE, Hu MY (2001) A simulation study of artificial neural networks for nonlinear time-series forecasting. Comput Oper Res 28(4):381–396. doi:10.1016/S0305-0548(99)00123-9

    Article  MATH  Google Scholar 

  23. Movagharnejad K, Mehdizadeh B, Banihashemi M, Masoud SK (2011) Forecasting the differences between various commercial oil prices in the Persian Gulf region by neural network. Energy 36(7):3979–3984. doi:10.1016/j.energy.2011.05.004

    Article  Google Scholar 

  24. Kulkarni S, Haidar I (2009) Forecasting model for crude oil price using artificial neural networks and commodity futures prices. Int J Comput Sci Inf Secur 2(1):1–8 arXiv:0906.4838

    Google Scholar 

  25. Xie W, Yu L, Xu S, Wang SY (2006) A new method for crude oil price forecasting based on support vector machines. In: Alexandrov VN, Albada GD, Sloot PMA, Dongarra J (eds) Computational science—ICC 2006. Springer, Berlin, pp 444–451. doi:10.1007/11758549_63

  26. Khashman A, Nwulu NI (2011) Intelligent prediction of crude oil price using support vector machines. In: Proceedings of the 2011 IEEE 9th international symposium on applied machine intelligence and informatics (SAMI), Smolenice; 2011, pp 165–169. doi:10.1109/SAMI.2011.5738868

  27. Li S, Ge Y (2013) Crude oil price prediction based on a dynamic correcting support vector regression machine. Abstr Appl Anal 2013:1–7. doi:10.1155/2013/528678

    MATH  Google Scholar 

  28. He K, Lai KK, Yen J (2010) A hybrid slantlet denoising least squares support vector regression model for exchange rate prediction. Proc Comput Sci 1(1):2397–2405. doi:10.1016/j.procs.2010.04.270

    Article  Google Scholar 

  29. Lin CS, Chiu SH, Lin TY (2012) Empirical mode decomposition-based least squares support vector regression for foreign exchange rate forecasting. Econ Model 29(6):2583–2590. doi:10.1016/j.econmod.2012.07.018

    Article  Google Scholar 

  30. Liu Y, Tao L, Lu J, Xu S, Ma Q, Duan Q (2011) A novel force field parameter optimization method based on LSSVR for ECEPP. FEBS Lett 585(6):888–892. doi:10.1016/j.febslet.2011.02.019

    Article  Google Scholar 

  31. Xie G, Wang S, Zhao Y, Lai KK (2013) Hybrid approaches based on LSSVR model for container throughput forecasting: a comparative study. Appl Soft Comput 13(5):2232–2241. doi:10.1016/j.asoc.2013.02.002

    Article  Google Scholar 

  32. Yang Z, Gu XS, Liang XY, Ling LC (2010) Genetic algorithm-least squares support vector regression based predicting and optimizing model on carbon fiber composite integrated conductivity. Mater Des 31(3):1042–1049. doi:10.1016/j.matdes.2009.09.057

    Article  Google Scholar 

  33. Tang L, Yu L, He K (2014) A novel data-characteristic-driven modeling methodology for nuclear energy consumption forecasting. Appl Energy 128(1):1–14. doi:10.1016/j.apenergy.2014.04.021

    Article  Google Scholar 

  34. Liao R, Zheng H, Grzybowski S, Yang L (2011) Particle swarm optimization-least squares support vector regression based forecasting model on dissolved gases in oil-filled power transformers. Electr Power Syst Res 81(12):2074–2080. doi:10.1016/j.epsr.2011.07.020

    Article  Google Scholar 

  35. Mellit A, Pavan AM, Benghanem M (2013) Least squares support vector machine for short-term prediction of meteorological time series. Theoret Appl Climatol 111(1–2):297–307. doi:10.1007/s00704-012-0661-7

    Article  Google Scholar 

  36. Wang J, Wang Y, Zhang C, Du W, Zhou C, Liang Y (2009) Parameter selection of support vector regression based on a novel chaotic immune algorithm. In: Proceedings of the 2009 IEEE fourth international conference on innovative computing, information and control (ICICIC), Kaohsiung, pp 652–655. doi:10.1109/ICICIC.2009.287

  37. Saini LM, Aggarwal SK, Kumar A (2010) Parameter optimisation using genetic algorithm for support vector machine-based price-forecasting model in national electricity market. Gener Transm Distrib IET 4(1):36–49. doi:10.1049/iet-gtd.2008.0584

    Article  Google Scholar 

  38. Ito K, Nakano R (2003) Optimizing support vector regression hyperparameters based on cross-validation. In: Proceedings of the IEEE international joint conference on neural networks, vol 3, pp 2077–2082. doi:10.1109/IJCNN.2003.1223728

  39. Zong Q, Liu W, Dou L (2006) Parameters selection for SVR based on PSO. In: Proceedings of the sixth IEEE world congress on intelligent control and automation, vol 1, pp 2811–2814. doi:10.1109/WCICA.2006.1712877

  40. Wang SY, Yu L, Lai KK (2005) Crude oil price forecasting with TEI@I methodology. J Syst Sci Complex 18(2):145–166 (in Chinese abstract)

    MathSciNet  MATH  Google Scholar 

  41. Parras-Gutierrez E, Garcia-Arenas M, Rivas VM, del Jesus MJ (2012) Coevolution of lags and RBFNs for time series forecasting: L-Co-R algorithm. Soft Comput 16(6):919–942. doi:10.1007/s00500-011-0784-2

    Article  Google Scholar 

  42. Parras-Gutierrez E, Rivas VM, Garcia-Arenas M, Del Jesus MJ (2014) Short, medium and long term forecasting of time series using the L-Co-R algorithm. Neurocomputing 128:433–446. doi:10.1016/j.neucom.2013.08.023

    Article  Google Scholar 

  43. Overmars MH (1988) Efficient data structures for range searching on a grid. J Algorithms 9(2):254–275. doi:10.1016/0196-6774(88)90041-7

    Article  MathSciNet  MATH  Google Scholar 

  44. Di Martino V, Mililotti M (2004) Sub optimal scheduling in a grid using genetic algorithms. Parallel Comput 30(5):553–565. doi:10.1016/j.parco.2003.12.004

    Article  Google Scholar 

  45. Huang CL, Wang CJ (2006) A GA-based feature selection and parameters optimization for support vector machines. Expert Syst Appl 31(2):231–240. doi:10.1016/j.eswa.2005.09.024

    Article  Google Scholar 

  46. Oliveira ALI, Braga PL, Lima RMF, Cornélio ML (2010) GA-based method for feature selection and parameters optimization for machine learning regression applied to software effort estimation. Inf Softw Technol 52(11):1155–1166. doi:10.1016/j.infsof.2010.05.009

    Article  Google Scholar 

  47. Yun Y, Gen M (2003) Performance analysis of adaptive genetic algorithms with fuzzy logic and heuristics. Fuzzy Optim Decis Mak 2(2):161–175. doi:10.1023/A:1023499201829

    Article  MathSciNet  Google Scholar 

  48. Duan K, Keerthi SS, Poo AN (2003) Evaluation of simple performance measures for tuning SVM hyperparameters. Neurocomputing 51:41–59. doi:10.1016/S0925-2312(02)00601-X

    Article  Google Scholar 

  49. Darwen PJ, Yao X (1997) Speciation as automatic categorical modularization. IEEE Trans Evol Comput 1(2):101–108. doi:10.1109/4235.687878

    Article  Google Scholar 

  50. McCall J, Petrovski A (1999) A decision support system for cancer chemotherapy using genetic algorithms. In: Proceedings of the international conference on computational intelligence for modeling, control and automation, pp 65–70

  51. Vapnik V (1995) The nature of statistical learning theory. Springer, New York

    Book  MATH  Google Scholar 

  52. Suykens JAK, Vandewalle J (1999) Least squares support vector machine classifiers. Neural Process Lett 9(3):293–300. doi:10.1023/A:1018628609742

    Article  MathSciNet  MATH  Google Scholar 

  53. Keerthi SS, Lin CJ (2003) Asymptotic behaviors of support vector machines with Gaussian kernel. Neural Comput 15(7):1667–1689. doi:10.1162/089976603321891855

    Article  MATH  Google Scholar 

  54. Bao Y, Liu Z (2006) A fast grid search method in support vector regression forecasting time series. In: Corchado E, Yin H, Botti V, Fyfe C (eds) Intelligent data engineering and automated learning-IDEAL. Springer, Berlin, pp 504–511. doi:10.1007/11875581_61

  55. Venkatesan D, Kannan K, Saravanan R (2009) A genetic algorithm-based artificial neural network model for the optimization of machining processes. Neural Comput Appl 18(2):135–140. doi:10.1007/s00521-007-0166-y

    Article  Google Scholar 

  56. Zhao M, Ren J, Ji L, Fu C, Li J, Zhou M (2012) Parameter selection of support vector machines and genetic algorithm based on change area search. Neural Comput Appl 21(1):1–8. doi:10.1007/s00521-011-0603-9

    Article  Google Scholar 

  57. Wu CH, Tzeng GH, Lin RH (2009) A novel hybrid genetic algorithm for kernel function and parameter optimization in support vector regression. Expert Syst Appl 36(3):4725–4735. doi:10.1016/j.eswa.2008.06.046

    Article  Google Scholar 

  58. Bao Y, Xiong T, Hu Z (2014) Multi-step-ahead time series prediction using multiple-output support vector regression. Neurocomputing 129:482–493. doi:10.1016/j.neucom.2013.09.010

    Article  Google Scholar 

  59. Xiong T, Bao Y, Hu Z (2013) Beyond one-step-ahead forecasting: evaluation of alternative multi-step-ahead forecasting models for crude oil prices. Energy Econ 40:405–415. doi:10.1016/j.eneco.2013.07.028

    Article  Google Scholar 

  60. Taieb SB, Bontempi G, Atiya AF, Sorjamaa A (2012) A review and comparison of strategies for multi-step ahead time series forecasting based on the NNs forecasting competition. Expert Syst Appl 39(8):7067–7083. doi:10.1016/j.eswa.2012.01.039

    Article  Google Scholar 

  61. Liu H, Tian H, Pan D, Li Y (2013) Forecasting models for wind speed using wavelet, wavelet packet, time series and artificial neural networks. Appl Energy 107:191–208. doi:10.1016/j.apenergy.2013.02.002

    Article  Google Scholar 

  62. Box GEP, Jenkins GM (1970) Time series analysis: forecasting and control, 1st edn. Holden Day, San Francisco

    MATH  Google Scholar 

  63. Yu L, Wang SY, Lai KK (2005) A novel nonlinear ensemble forecasting model incorporating GLAR and ANN for foreign exchange rates. Comput Oper Res 32(10):2523–2541. doi:10.1016/j.cor.2004.06.024

    Article  MATH  Google Scholar 

  64. Rezaeianzadeh M, Tabari H, Yazdi AA, Isik S, Kalin L (2014) Flood flow forecasting using ANN, ANFIS and regression models. Neural Comput Appl 25(1):25–37. doi:10.1007/s00521-013-1443-6

    Article  Google Scholar 

  65. Sudheer C, Maheswaran R, Panigrahi BK, Mathur S (2014) A hybrid SVM-PSO model for forecasting monthly streamflow. Neural Comput Appl 24(6):1381–1389. doi:10.1007/s00521-013-1341-y

    Article  Google Scholar 

  66. Kennedy JF, Kennedy J, Eberhart RC (2001) Swarm intelligence. Morgan Kaufmann, San Francisco

    Google Scholar 

  67. Marinakis Y, Marinaki M, Doumpos M, Zopounidis C (2009) Ant colony and particle swarm optimization for financial classification problems. Expert Syst Appl 36(7):10604–10611. doi:10.1016/j.eswa.2009.02.055

    Article  Google Scholar 

  68. Eberhart RC, Shi Y (2001) Particle swarm optimization: developments, applications and resources. In: Proceedings of the 2001 IEEE congress on evolutionary computation, vol 1, pp 81–86. doi:10.1109/CEC.2001.934374

  69. Ekren O, Ekren BY (2010) Size optimization of a PV/wind hybrid energy conversion system with battery storage using simulated annealing. Appl Energy 87(2):592–598. doi:10.1016/j.apenergy.2009.05.022

    Article  Google Scholar 

  70. Li JP, Tang L, Sun XL, Yu L, He W, Yang YY (2012) Country risk forecasting for major oil exporting countries: a decomposition hybrid approach. Comput Ind Eng 63(3):641–651. doi:10.1016/j.cie.2011.12.003

    Article  Google Scholar 

  71. Liu H, Tian H, Li Y (2012) Comparison of two new ARIMA-ANN and ARIMA-Kalman hybrid methods for wind speed prediction. Appl Energy 98:415–424. doi:10.1016/j.apenergy.2012.04.001

    Article  Google Scholar 

  72. Tay FEH, Cao L (2001) Application of support vector machines in financial time series forecasting. Omega 29(4):309–317. doi:10.1016/S0305-0483(01)00026-3

    Article  Google Scholar 

  73. Yu L, Wang SY, Lai KK (2007) Foreign-exchange-rate forecasting with artificial neural networks. Springer, Boston

    Book  MATH  Google Scholar 

  74. Li X, He K, Lai KK, Zou YC (2014) Forecasting crude oil price with multiscale denoising ensemble model. Math Probl Eng 4:1–28. doi:10.1155/2014/716571

    Google Scholar 

  75. Wang SY, Yu L, Lai KK (2005) A novel hybrid AI system framework for crude oil price forecasting. In: Shi Y, Xu WX, Chen ZHX (eds) Data mining and knowledge management. Springer, Berlin, pp 233–242. doi:10.1007/978-3-540-30537-8_26

  76. Wolpert DH, Macready WG (1997) No free lunch theorems for optimization. IEEE Trans Evol Comput 1(1):67–82. doi:10.1109/4235.585893

    Article  Google Scholar 

  77. Wolpert DH, Macready WG (2005) Coevolutionary free lunches. IEEE Trans Evol Comput 9(6):721–735. doi:10.1109/TEVC.2005.856205

    Article  Google Scholar 

  78. Rowe JE, Vose MD, Wright AH (2009) Reinterpreting no free lunch. Evol Comput 17(1):117–129. doi:10.1162/evco.2009.17.1.117

    Article  Google Scholar 

  79. Hansen N, Müller SD, Koumoutsakos P (2003) Reducing the time complexity of the derandomized evolution strategy with covariance matrix adaptation (CMA-ES). Evol Comput 11(1):1–18. doi:10.1162/106365603321828970

    Article  Google Scholar 

  80. Tang L, Dai W, Yu L (2015) A novel CEEMD-based EELM ensemble learning paradigm for crude oil price forecasting. Int J Inf Technol Decis Mak 14(1):141–169. doi:10.1142/S0219622015400015

    Article  Google Scholar 

  81. Yu L, Zhao Y, Tang L (2014) A compressed sensing based AI learning paradigm for crude oil price forecasting. Energy Econ 46:236–245. doi:10.1016/j.eneco.2014.09.019

    Article  Google Scholar 

  82. Yu L, Dai W, Tang L (2015) A novel decomposition ensemble model with extended extreme learning machine for crude oil price forecasting. Eng Appl Artif Intell. doi:10.1016/j.engappai.2015.04.016

    Google Scholar 

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Acknowledgments

This work is partially supported by grants from the National Science Fund for Distinguished Young Scholars (NSFC No. 71025005), the National Natural Science Foundation of China (NSFC No. 91224001 and NSFC No. 71301006), the National Program for Support of Top-Notch Young Professionals and the Fundamental Research Funds for the Central Universities in BUCT. Authors would like to express their sincere appreciation to the editor and the three independent referees in making valuable comments and suggestions to the paper. Their comments have improved the quality of the paper immensely.

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  1. School of Economics and Management, Beijing University of Chemical Technology, 15 Beisanhuan East Road, Beijing, 100029, China

    Lean Yu, Wei Dai, Ling Tang & Jiaqian Wu

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  1. Lean Yu
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Correspondence to Ling Tang.

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Yu, L., Dai, W., Tang, L. et al. A hybrid grid-GA-based LSSVR learning paradigm for crude oil price forecasting. Neural Comput & Applic 27, 2193–2215 (2016). https://doi.org/10.1007/s00521-015-1999-4

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