IDEAS home Printed from https://ideas.repec.org/a/eee/tefoso/v112y2016icp245-253.html
   My bibliography  Save this article

An ICA-based support vector regression scheme for forecasting crude oil prices

Author

Listed:
  • Fan, Liwei
  • Pan, Sijia
  • Li, Zimin
  • Li, Huiping

Abstract

The fluctuations of crude oil prices affect the economic growth of importing and exporting countries as well as regional security and stability. The intrinsic complex features of oil prices and the uncertainty in economic policy pose challenge on the accurate forecasting of crude oil prices. This paper employs independent component analysis (ICA) to analyze crude oil prices which are decomposed into several independent components corresponding to different types of influential factors affecting oil price. We also propose a novel ICA-based support vector regression scheme, namely ICA-SVR2, for forecasting crude oil prices. The ICA-SVR2 starts from the use of ICA to decompose oil price series into three independent components, which are respectively forecasted by SVR models. The forecasted independent components are then integrated together by developing a new SVR model with independent components as inputs for forecasting crude oil prices. Our experimental results show the usefulness of ICA in identifying the driving factors behind the fluctuations of crude oil prices. A comparative study between ICA-SVR2 and other two models shows that ICA-SVR2 is an effective tool in forecasting crude oil prices.

Suggested Citation

  • Fan, Liwei & Pan, Sijia & Li, Zimin & Li, Huiping, 2016. "An ICA-based support vector regression scheme for forecasting crude oil prices," Technological Forecasting and Social Change, Elsevier, vol. 112(C), pages 245-253.
    • Handle: RePEc:eee:tefoso:v:112:y:2016:i:c:p:245-253
    DOI: 10.1016/j.techfore.2016.04.027
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0040162516300579
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.techfore.2016.04.027?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. 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.
    2. Liwei Fan & Huiping Li, 2015. "Volatility analysis and forecasting models of crude oil prices: a review," International Journal of Global Energy Issues, Inderscience Enterprises Ltd, vol. 38(1/2/3), pages 5-17.
    3. Alvarez-Ramirez, Jose & Rodriguez, Eduardo & Martina, Esteban & Ibarra-Valdez, Carlos, 2012. "Cyclical behavior of crude oil markets and economic recessions in the period 1986–2010," Technological Forecasting and Social Change, Elsevier, vol. 79(1), pages 47-58.
    4. 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.
    5. Zhu, Bangzhu & Wei, Yiming, 2013. "Carbon price forecasting with a novel hybrid ARIMA and least squares support vector machines methodology," Omega, Elsevier, vol. 41(3), pages 517-524.
    6. Bekiros, Stelios & Gupta, Rangan & Paccagnini, Alessia, 2015. "Oil price forecastability and economic uncertainty," Economics Letters, Elsevier, vol. 132(C), pages 125-128.
    7. Zhang, Xun & Yu, Lean & Wang, Shouyang & Lai, Kin Keung, 2009. "Estimating the impact of extreme events on crude oil price: An EMD-based event analysis method," Energy Economics, Elsevier, vol. 31(5), pages 768-778, September.
    8. 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.
    9. 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.
    10. 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.
    11. 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.
    12. Hitaj, Asmerilda & Mercuri, Lorenzo & Rroji, Edit, 2015. "Portfolio selection with independent component analysis," Finance Research Letters, Elsevier, vol. 15(C), pages 146-159.
    13. Azadeh, A. & Asadzadeh, S.M. & Mirseraji, G.H. & Saberi, M., 2015. "An emotional learning-neuro-fuzzy inference approach for optimum training and forecasting of gas consumption estimation models with cognitive data," Technological Forecasting and Social Change, Elsevier, vol. 91(C), pages 47-63.
    14. Wang, Yudong & Wu, Chongfeng & Yang, Li, 2016. "Forecasting crude oil market volatility: A Markov switching multifractal volatility approach," International Journal of Forecasting, Elsevier, vol. 32(1), pages 1-9.
    15. Mostafa, Mohamed M. & El-Masry, Ahmed A., 2016. "Oil price forecasting using gene expression programming and artificial neural networks," Economic Modelling, Elsevier, vol. 54(C), pages 40-53.
    16. Lutao Zhao & Lei Cheng & Yongtao Wan & Hao Zhang & Zhigang Zhang, 2015. "A VAR-SVM model for crude oil price forecasting," International Journal of Global Energy Issues, Inderscience Enterprises Ltd, vol. 38(1/2/3), pages 126-144.
    17. Tay, Francis E. H. & Cao, Lijuan, 2001. "Application of support vector machines in financial time series forecasting," Omega, Elsevier, vol. 29(4), pages 309-317, August.
    18. Wu, Gang & Zhang, Yue-Jun, 2014. "Does China factor matter? An econometric analysis of international crude oil prices," Energy Policy, Elsevier, vol. 72(C), pages 78-86.
    19. Zhang, Yue-Jun, 2013. "Speculative trading and WTI crude oil futures price movement: An empirical analysis," Applied Energy, Elsevier, vol. 107(C), pages 394-402.
    20. Kizilaslan, Recep & Freund, Steven & Iseri, Ali, 2016. "A data analytic approach to forecasting daily stock returns in an emerging marketAuthor-Name: Oztekin, Asil," European Journal of Operational Research, Elsevier, vol. 253(3), pages 697-710.
    21. Zhang, Yue-Jun & Wang, Zi-Yi, 2013. "Investigating the price discovery and risk transfer functions in the crude oil and gasoline futures markets: Some empirical evidence," Applied Energy, Elsevier, vol. 104(C), pages 220-228.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Chai, Jian & Xing, Li-Min & Zhou, Xiao-Yang & Zhang, Zhe George & Li, Jie-Xun, 2018. "Forecasting the WTI crude oil price by a hybrid-refined method," Energy Economics, Elsevier, vol. 71(C), pages 114-127.
    2. Lin, Ling & Jiang, Yong & Xiao, Helu & Zhou, Zhongbao, 2020. "Crude oil price forecasting based on a novel hybrid long memory GARCH-M and wavelet analysis model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 543(C).
    3. Ju, Keyi & Su, Bin & Zhou, Dequn & Wu, Junmin & Liu, Lifan, 2016. "Macroeconomic performance of oil price shocks: Outlier evidence from nineteen major oil-related countries/regions," Energy Economics, Elsevier, vol. 60(C), pages 325-332.
    4. Ding, Yishan, 2018. "A novel decompose-ensemble methodology with AIC-ANN approach for crude oil forecasting," Energy, Elsevier, vol. 154(C), pages 328-336.
    5. Cheng, Fangzheng & Li, Tian & Wei, Yi-ming & Fan, Tijun, 2019. "The VEC-NAR model for short-term forecasting of oil prices," Energy Economics, Elsevier, vol. 78(C), pages 656-667.
    6. Taiyong Li & Zhenda Hu & Yanchi Jia & Jiang Wu & Yingrui Zhou, 2018. "Forecasting Crude Oil Prices Using Ensemble Empirical Mode Decomposition and Sparse Bayesian Learning," Energies, MDPI, vol. 11(7), pages 1-23, July.
    7. 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.
    8. Zhao, Geya & Xue, Minggao & Cheng, Li, 2023. "A new hybrid model for multi-step WTI futures price forecasting based on self-attention mechanism and spatial–temporal graph neural network," Resources Policy, Elsevier, vol. 85(PB).
    9. 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.
    10. Yao, Ting & Zhang, Yue-Jun & Ma, Chao-Qun, 2017. "How does investor attention affect international crude oil prices?," Applied Energy, Elsevier, vol. 205(C), pages 336-344.
    11. Li, Jinchao & Zhu, Shaowen & Wu, Qianqian, 2019. "Monthly crude oil spot price forecasting using variational mode decomposition," Energy Economics, Elsevier, vol. 83(C), pages 240-253.
    12. Ju, Keyi & Su, Bin & Zhou, Dequn & Zhang, Yuqiang, 2016. "An incentive-oriented early warning system for predicting the co-movements between oil price shocks and macroeconomy," Applied Energy, Elsevier, vol. 163(C), pages 452-463.
    13. 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.
    14. Zhang, Yue-Jun & Yao, Ting & He, Ling-Yun & Ripple, Ronald, 2019. "Volatility forecasting of crude oil market: Can the regime switching GARCH model beat the single-regime GARCH models?," International Review of Economics & Finance, Elsevier, vol. 59(C), pages 302-317.
    15. Wang, Minggang & Zhao, Longfeng & Du, Ruijin & Wang, Chao & Chen, Lin & Tian, Lixin & Eugene Stanley, H., 2018. "A novel hybrid method of forecasting crude oil prices using complex network science and artificial intelligence algorithms," Applied Energy, Elsevier, vol. 220(C), pages 480-495.
    16. Emmanuel Senyo Fianu, 2022. "Analyzing and Forecasting Multi-Commodity Prices Using Variants of Mode Decomposition-Based Extreme Learning Machine Hybridization Approach," Forecasting, MDPI, vol. 4(2), pages 1-27, June.
    17. Abdollahi, Hooman & Ebrahimi, Seyed Babak, 2020. "A new hybrid model for forecasting Brent crude oil price," Energy, Elsevier, vol. 200(C).
    18. Polanco Martínez, Josué M. & Abadie, Luis M. & Fernández-Macho, J., 2018. "A multi-resolution and multivariate analysis of the dynamic relationships between crude oil and petroleum-product prices," Applied Energy, Elsevier, vol. 228(C), pages 1550-1560.
    19. Sun, Shaolong & Sun, Yuying & Wang, Shouyang & Wei, Yunjie, 2018. "Interval decomposition ensemble approach for crude oil price forecasting," Energy Economics, Elsevier, vol. 76(C), pages 274-287.
    20. Wang, Jun & Cao, Junxing & Yuan, Shan & Cheng, Ming, 2021. "Short-term forecasting of natural gas prices by using a novel hybrid method based on a combination of the CEEMDAN-SE-and the PSO-ALS-optimized GRU network," Energy, Elsevier, vol. 233(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:tefoso:v:112:y:2016:i:c:p:245-253. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.sciencedirect.com/science/journal/00401625 .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.