Skip to main content
Fachgebiete
Automobil + Motoren Bauwesen + Immobilien Business IT + Informatik Elektrotechnik + Elektronik Energie + Nachhaltigkeit Finance + Banking Management + Führung Marketing + Vertrieb Maschinenbau + Werkstoffe Versicherung + Risiko
DE
EN
Bücher
Zeitschriften
Themenseiten
Organisationspsychologie Projektmanagement Marketing Smart Manufacturing
Weitere Formate
Podcasts Webinare Technik Webinare Wirtschaft Kongresse Veranstaltungskalender Awards
Jetzt Einzelzugang starten
Zugang für Unternehmen
Referenzkunden
SLX-Digitalkonferenz: Zukunftswerkstatt 2024

Mehr Umsatz mit Top-Kontakten

Am 7. Mai erfahren Sie, wie Vertriebsteams Leadgenerierung, Leadnurturing und Leadstrategien effizient steuern können.
Erweiterte Suche
Anmelden
nach oben
Energy Systems
Erschienen in: Energy Systems 4/2015

01.11.2015 | Original Paper

Artificial intelligence methods for oil price forecasting: a review and evaluation

verfasst von: Neha Sehgal, Krishan K. Pandey

Erschienen in: Energy Systems | Ausgabe 4/2015

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config
loading …

Abstract

Artificial intelligent methods are being extensively used for oil price forecasting as an alternate approach to conventional techniques. There has been a whole spectrum of artificial intelligent techniques to overcome the difficulties of complexity and irregularity in oil price series. The potential of AI as a design tool for oil price forecasting has been reviewed in this study. The following price forecasting techniques have been covered: (i) artificial neural network, (ii) support vector machine, (iii) wavelet, (iv) genetic algorithm, and (v) hybrid systems. In order to investigate the state of artificial intelligent models for oil price forecasting, thirty five research papers (published during 2001 to 2013) had been reviewed in form of table (for ease of comparison) based on the following parameters: (a) input variables, (b) input variables selection method, (c) data characteristics (d) forecasting accuracy and (e) model architecture. This review reveals procedure of AI methods used in complex oil price related studies. The review further extended above overview into discussions regarding specific shortcomings that are associated with feature selection for designing input vector, and then concluded with future insight on improving the current state-of-the-art technology.
Vorheriger Artikel Electrical distribution network operation with a presence of distributed generation units in a Pre Smart Grid environment using a clustering-based methodology
Nächster Artikel International experience with compressed natural gas (CNG) as environmental friendly fuel

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Jetzt informieren
Literatur
1.
British Petroleum. Statistical review of world energy 2013. British Petroleum (2013)
2.
International Energy Agency. World Energy Outlook, 2012. OECD/IEA (2012)
3.
Benning, C., Pichersky, E.: Harnessing plant biomass for biofuels and biomaterials. Plant J. 54(4), 533–535 (2008)CrossRef
4.
Andreou, E., Pittis, N., Spanos, A.: On modelling speculative prices: the empirical literature. J. Econ. Surv. 15(2), 187–220 (2001)CrossRef
5.
Arouri, M.E.H., Lahiani, A., Lévy, A., Nguyen, D.K.: Forecasting the conditional volatility of oil spot and futures prices with structural breaks and long memory models. Energy Econ. 34(1), 283–293 (2012)CrossRef
6.
Bing, X., Ouenniche, J.: A data envelopment analysis-based framework for the relative performance evaluation of competing crude oil prices’ volatility forecasting models. Energy Econ. 34(2), 576–583 (2012)CrossRef
7.
He, K., Yu, L., Lai, K.K.: Crude oil price analysis and forecasting using wavelet decomposed ensemble model. Energy 46(1):564–574 (2012)
8.
Mohammadi, H., Lixian, S.: International evidence on crude oil price dynamics: applications of ARIMA-GARCH models. Energy Econ. 32(5), 1001–1008 (2010)CrossRef
9.
Tian, Z., Swanson, N.R.: Predictive evaluation of econometric forecasting models in commodity futures markets. Stud. Nonlinear Dyn. Econom. 2(4), 159–177 (1998)
10.
Zhang, X., Wu, Q., Zhang, J.: Crude oil price forecasting using fuzzy time series. In: 2010 3rd International Symposium on Knowledge Acquisition and Modeling (KAM), pp. 213–216. IEEE (2010)
11.
Haidar, I., Kulkarni, S., Pan, H.: Forecasting model for crude oil prices based on artificial neural networks. In: International Conference on Intelligent Sensors, Sensor Networks and Information Processing, 2008 (ISSNIP 2008), pp. 103–108 (2008)
12.
Chinn, MD., LeBlanc, M., Coibion, O.: The predictive content of energy futures: an update on petroleum, natural gas, heating oil and gasoline. Technical report, National Bureau of Economic Research (2005)
13.
Alvarez-Ramirez, J., Rodriguez, E., Martina, E., Ibarra-Valdez, C.: Cyclical behavior of crude oil markets and economic recessions in the period 1986–2010. Technol. Forecast. Soc. Change 79(1), 47–58 (2012)CrossRef
14.
Movagharnejad, K., Mehdizadeh, B., Banihashemi, M., Kordkheili, M.S.: Forecasting the differences between various commercial oil prices in the persian gulf region by neural network. Energy 36(7), 3979–3984 (2011)
15.
Weiqi, L., Linwei, M., Yaping, D., Pei, L.: An econometric modeling approach to short-term crude oil price forecasting. In: Control Conference (CCC), 2011 30th Chinese, pp. 1582–1585. IEEE (2011)
16.
Dées, S., Gasteuil, A., Kaufmann, R., Mann, M.: Assessing the factors behind oil price changes (2008)
17.
Jinliang, Z., Mingming, T., Mingxin, T.: Effects simulation of international gold prices on crude oil prices based on WBNNK model. In: ISECS International Colloquium on Computing, Communication, Control, and Management, 2009 (CCCM 2009), vol. 4, pp. 459–463 (2009)
18.
Abdullah, S.N. Zeng, X.: Machine learning approach for crude oil price prediction with artificial neural networks-quantitative (ann-q) model. In: The 2010 International Joint Conference on Neural Networks (IJCNN), pp. 1–8 (2010)
19.
Krichene, N.: Crude oil prices: trends and forecast. IMF Working Papers, pp. 1–23 (2008)
20.
de Souza e Silva, E.G., Legey, L.F.L., de Souza e Silva, E.A.: Forecasting oil price trends using wavelets and hidden markov models. Energy Econ. 32(6), 1507–1519 (2010)CrossRef
21.
Chernenko, S.V., Schwarz, K.B., Wright, J.H.: The information content of forward and futures prices: market expectations and the price of risk. Board of Governors of the Federal Reserve System (2004)
22.
He, A.W.W., Kwok, J.T.K., Wan, A.T.K.: An empirical model of daily highs and lows of west texas intermediate crude oil prices. Energy Econ. 32(6), 1499–1506 (2010)CrossRef
23.
Pindyck, R.S.: The long-run evolution of energy prices. The Energy Journal. 20, 1–27 (1999)
24.
Eduardo, S., Smith, J.E.: Short-term variations and long-term dynamics in commodity prices. Manag. Sci. 46(7), 893–911 (2000)CrossRef
25.
Mazraati, Mohammad, Jazayeri, S.M.: Oil price movements and production agreements. OPEC Rev. 28(3), 207–226 (2004)CrossRef
26.
Moshiri, S., Foroutan, F.: Forecasting nonlinear crude oil futures prices. Energy J. 27(4), 81–96 (2006)CrossRef
27.
Radziukyniene, I., Boyko, N., Pardalos, P.: Model-based forecasting. Wiley encyclopedia of operations research and management science, vol. 5, pp. 3305–3312 (2011)
28.
Hou, A., Suardi, S.: A nonparametric GARCH model of crude oil price return volatility. Energy Econ. 34(2), 618–626 (2012)CrossRef
29.
Sang, H.K., Kang, S.M., Yoon, S.M.: Forecasting volatility of crude oil markets. Energy Econ. 31(1), 119–125 (2009)CrossRef
30.
Wei, Y., Wang, Y., Huang, D.: Forecasting crude oil market volatility: further evidence using GARCH-class models. Energy Econ. 32(6), 1477–1484 (2010)CrossRef
31.
Huang, B.-N., Yang, C.W., Hwang, M.J.: The dynamics of a nonlinear relationship between crude oil spot and futures prices: a multivariate threshold regression approach. Energy Econ. 31(1), 91–98 (2009)CrossRef
32.
Lixia, L.: Nonlinear test and forecasting of petroleum futures prices time series. Energy Procedia 5, 754–758 (2011)CrossRef
33.
Kisswani, K.M., Nusair, S.A.: Non-linearities in the dynamics of oil prices. Energy Econ. 36, 341–353 (2013)CrossRef
34.
James, H.D.: Nonlinearities and the macroeconomic effects of oil prices. Macroecon. Dyn. 15(S3), 364–378 (2011)CrossRef
35.
Ye, M., Zyren, J., Shore, J.: Forecasting crude oil spot price using OECD petroleum inventory levels. Int. Adv. Econ. Res. 8(4), 324–333 (2002)CrossRef
36.
Ye, M., Zyren, J., Shore, J.: A monthly crude oil spot price forecasting model using relative inventories. Int. J. Forecast. 21(3), 491–501 (2005)CrossRef
37.
Ye, M., Zyren, J., Shore, J.: Forecasting short-run crude oil price using high-and low-inventory variables. Energy Policy 34(17), 2736–2743 (2006)CrossRef
38.
Zamani, M.: An econometrics forecasting model of short term oil spot price. In: 6th IAEE European Conference, Citeseer (2004)
39.
Chai, J., Guo, J.E., Meng, L., Wang, S.Y.: Exploring the core factors and its dynamic effects on oil price: An application on path analysis and BVAR-TVP model. Energy Policy 39(12), 8022–8036 (2011)
40.
Frey, G., Manera, M., Markandya, A., Scarpa, E.: Econometric models for oil price forecasting: a critical survey. In: CESifo Forum, vol. 10, pp. 29–44. Institute for Economic Research at the University of Munich (2009)
41.
Juan, R.C.: How do crude oil prices co-move?: A copula approach. Energy Econ. 33(5), 948–955 (2011)CrossRef
42.
International Energy Agency. Oil Market Report, 2014. OECD/IEA (2014)
43.
Pang, Y., Wei, X., Lean, Y., Ma, J., Lai, K.K., Wang, S., Xu, S.: Forecasting the crude oil spot price by wavelet neural networks using OECD petroleum inventory levels. New Math. Nat. Comput. 07(02), 281–297 (2011)CrossRef
44.
Timothy, C.J., Heo, E.: Price and inventory dynamics in petroleum product markets. Energy Econ. 22(5), 527–548 (2000)CrossRef
45.
Kim, D.H.: What is an oil shock? Panel data evidence. Empir. Econ. 43(1), 121–143 (2012)CrossRef
46.
Dées, S., Karadeloglou, P., Kaufmann, R.K., Sanchez, M.: Modelling the world oil market: assessment of a quarterly econometric model. Energy Policy 35(1), 178–191 (2007)CrossRef
47.
Ratti, R.A., Vespignani, J.L.: Crude oil prices and liquidity, the BRIC and G3 countries. Energy Econ. 39, 28–38 (2013)CrossRef
48.
Li, H., Lin, S.X.: Do emerging markets matter in the world oil pricing system? Evidence of imported crude by China and India. Energy Policy 39(8), 4624–4630 (2011)
49.
Zhang, Y.-J., Wang, Z.-Y.: Investigating the price discovery and risk transfer functions in the crude oil and gasoline futures markets: some empirical evidence. Appl. Energy 104, 220–228 (2013)
50.
Murat, A., Tokat, E.: Forecasting oil price movements with crack spread futures. Energy Econ. 31(1), 85–90 (2009)CrossRef
51.
Yang, W., Han, A., Cai, K., Wang, S.: ACIX model with interval dummy variables and its application in forecasting interval-valued crude oil prices. Procedia Comput. Sci. 9, 1273–1282 (2012)CrossRef
52.
He, Y., Wang, S., Lai, K.K.: Global economic activity and crude oil prices: a cointegration analysis. Energy Econ. 32(4), 868–876 (2010)
53.
Hui, B.: Price dynamics and speculators in crude oil futures market. Syst. Eng. Procedia 2, 114–121 (2011)CrossRef
54.
Syed Abul, B., Haug, A.A., Sadorsky, P.: Oil prices, exchange rates and emerging stock markets. Energy Econ. 34(1), 227–240 (2012)CrossRef
55.
Zhang, X., Yu, L., Wang, S., Lai, K.K.: Estimating the impact of extreme events on crude oil price: an EMD-based event analysis method. Energy Econ. 31(5), 768–778 (2009)
56.
Fong, W.M., See, K.H.: A markov switching model of the conditional volatility of crude oil futures prices. Energy Econ. 24(1), 71–95 (2002)
57.
Haidar, I., Kulkarni, S., Pan, H.: Forecasting model for crude oil prices based on artificial neural networks. In: International Conference on Intelligent Sensors, Sensor Networks and Information Processing, 2008 (ISSNIP 2008), pp. 103–108. IEEE (2008)
58.
Mahdi, A.A., Hussain, A.J., Al-Jumeily, D.: Adaptive neural network model using the immune system for financial time series forecasting. In: International Conference on Computational Intelligence, Modelling and Simulation, 2009 (CSSim ’09), pp. 104–109 (2009)
59.
Bao, Y., Yang, Y., Xiong, T., Zhang, J.: A comparative study of multi-step-ahead prediction for crude oil price with support vector regression. In: 2011 Fourth International Joint Conference on Computational Sciences and Optimization (CSO), pp. 598–602 (2011)
60.
Khashman, A., Nwulu, N.I.: Intelligent prediction of crude oil price using support vector machines. In: 2011 IEEE 9th International Symposium on Applied Machine Intelligence and Informatics (SAMI), pp. 165–169 (2011)
61.
He, K., Lai, K.K., Yen, J.: Crude oil price prediction using slantlet denoising based hybrid models. In: International Joint Conference on Computational Sciences and Optimization, 2009 (CSO 2009), vol. 2, pp. 12–16 (2009)
62.
Zhu, J.-R.: A new model for oil futures price forecasting based on cluster analysis. In: 4th International Conference on Wireless Communications, Networking and Mobile Computing, 2008 (WiCOM ’08), pp. 1–4 (2008)
63.
He, K., Lai, K.K., Yen, J.: Morphological component analysis based hybrid approach for prediction of crude oil price. In: 2010 Third International Joint Conference on Computational Science and Optimization (CSO), vol. 1, pp. 423–427 (2010)
64.
Azadeh, A., Moghaddam, M., Khakzad, M., Ebrahimipour, V.: A flexible neural network-fuzzy mathematical programming algorithm for improvement of oil price estimation and forecasting. Comput. Ind. Eng. 62(2), 421–430 (2012)CrossRef
65.
Malliaris, M.E., Malliaris, S.G.: Forecasting energy product prices. In: Proceedings of 2005 IEEE International Joint Conference on Neural Networks, 2005 (IJCNN ’05), vol. 5, pp. 3284–3289 (2005)
66.
Alizadeh, A., Mafinezhad, K.: Monthly brent oil price forecasting using artificial neural networks and a crisis index. In: 2010 International Conference on Electronics and Information Engineering (ICEIE), vol. 2, pp. V2-465–V2-468. IEEE (2010)
67.
Xie, W., Lean, Y., Shanying, X., Wang, S.: A new method for crude oil price forecasting based on support vector machines. In: Computational Science ICCS 2006. Lecture Notes in Computer Science, vol. 3994, pp. 444–451. Springer, Berlin (2006)
68.
Kaboudan, M.A.: Compumetric forecasting of crude oil prices. In: Proceedings of the 2001 Congress on Evolutionary Computation, 2001, vol. 1, pp. 283–287. IEEE (2001)
69.
Amin-Naseri, M.R., Gharacheh, E.A.: A hybrid artificial intelligence approach to monthly forecasting of crude oil price time series. In: The Proceedings of the 10th International Conference on Engineering Applications of Neural Networks, CEUR-WS284, pp. 160–167 (2007)
70.
Xiao, J., He, C., Wang, S.: Crude oil price forecasting: a transfer learning based analog complexing model. In: 2012 Fifth International Conference on Business Intelligence and Financial Engineering (BIFE), pp. 29–33. IEEE (2012)
71.
Yousefi, S., Weinreich, I., Reinarz, D.: Wavelet-based prediction of oil prices. Chaos Solitons Fractals 25(2), 265–275 (2005)MATHCrossRef
72.
Alexandridis, A., Livanis, E.: Forecasting crude oil prices using wavelet neural networks. In: Proceedings of the 5th FSDET (\(\Phi \,\Sigma \,\Delta \)ET), Athens, Greece, pp. 8 (2008)
73.
Fan, Y., Liang, Q., Wei, Y.-M.: A generalized pattern matching approach for multi-step prediction of crude oil price. Energy Econ. 30(3), 889–904 (2008)CrossRef
74.
Mingming, T., Jinliang, Z.: A multiple adaptive wavelet recurrent neural network model to analyze crude oil prices. J. Econ. Bus. 64(4), 275–286 (2012)CrossRef
75.
He, K., Xie, C., Chen, S., Lai, K.K.: Estimating VaR in crude oil market: a novel multi-scale non-linear ensemble approach incorporating wavelet analysis and neural network. Neurocomputing 72, 3428–3438 (2009)
76.
Jammazi, R., Aloui, C.: Crude oil price forecasting: experimental evidence from wavelet decomposition and neural network modeling. Energy Econ. 34(3), 828–841 (2012)CrossRef
77.
Qunli, W., Ge, H., Xiaodong, C.: Crude oil price forecasting with an improved model based on wavelet transform and RBF neural network. In: International Forum on Information Technology and Applications, 2009 (IFITA ’09), vol. 1, pp. 231–234 (2009)
78.
Panella, M., Liparulo, L., Barcellona, F., D’Ecclesia, R.L.: A study on crude oil prices modeled by neurofuzzy networks. In: 2013 IEEE International Conference on Fuzzy Systems (FUZZ), pp. 1–7 (2013)
79.
Ghaffari, A., Zare, S.: A novel algorithm for prediction of crude oil price variation based on soft computing. Energy Econ. 31(4), 531–536 (2009)CrossRef
80.
Yu, L., Wang, S., Lai, K.K.L Forecasting crude oil price with an EMD-based neural network ensemble learning paradigm. Energy Econ. 30(5), 2623–2635 (2008)
81.
Xiong, T., Bao, Y., Zhongyi, H.: Beyond one-step-ahead forecasting: evaluation of alternative multi-step-ahead forecasting models for crude oil prices. Energy Econ. 40, 405–415 (2013)CrossRef
82.
Guo, X., Li, D.C., Zhang, A.: Improved support vector machine oil price forecast model based on genetic algorithm optimization parameters. AASRI Procedia 1, 525–530 (2012)CrossRef
83.
Gabralla, L.A., Jammazi, R., Abraham, A.: Oil price prediction using ensemble machine learning. In: 2013 International Conference on Computing, Electrical and Electronics Engineering (ICCEEE), pp. 674–679. IEEE (2013)
84.
Sadorsky, P.: Oil price shocks and stock market activity. Energy Econ. 21(5), 449–469 (1999)CrossRef
85.
Lin, A.: Prediction of international crude oil futures price based on GM (1, 1). In: IEEE International Conference on Grey Systems and Intelligent Services, 2009 (GSIS 2009), pp. 692–696. IEEE (2009)
86.
Chang, K.L.: Volatility regimes, asymmetric basis effects and forecasting performance: an empirical investigation of the WTI crude oil futures market. Energy Econ. 34(1), 294–306 (2012)
87.
Vo, M.T.: Regime-switching stochastic volatility: evidence from the crude oil market. Energy Econ. 31(5), 779–788 (2009)CrossRef
88.
Liu, L., Wan, J.: A study of shanghai fuel oil futures price volatility based on high frequency data: long-range dependence, modeling and forecasting. Econ. Model. 29(6), 2245–2253 (2012)CrossRef
89.
Zhang, X., Lai, K.K., Wang, S.-Y.: A new approach for crude oil price analysis based on empirical mode decomposition. Energy Econ. 30(3), 905–918 (2008)CrossRef
90.
Huang, D., Baimin, Y., Fabozzi, F.J., Fukushima, M.: CAViar-based forecast for oil price risk. Energy Econ. 31(4), 511–518 (2009)CrossRef
91.
Wei, Y.: Forecasting volatility of fuel oil futures in China: GARCH-type, SV or realized volatility models? Phys. A Stat. Mech. Appl. 391(22), 5546–5556 (2012)CrossRef
92.
Chih, C.W., Chung, H., Chang, Y.H.: The economic value of co-movement between oil price and exchange rate using copula-based GARCH models. Energy Econ. 34(1), 270–282 (2012)CrossRef
93.
Yang, C.W., Hwang, M.-J., Huang, B.-N.: An analysis of factors affecting price volatility of the US oil market. Energy Econ. 24(2), 107–119 (2002)CrossRef
Metadaten
Titel
Artificial intelligence methods for oil price forecasting: a review and evaluation
verfasst von
Neha Sehgal
Krishan K. Pandey
Publikationsdatum
01.11.2015
Verlag
Springer Berlin Heidelberg
Erschienen in
Energy Systems / Ausgabe 4/2015
Print ISSN: 1868-3967
Elektronische ISSN: 1868-3975
DOI
https://doi.org/10.1007/s12667-015-0151-y

Weitere Artikel der Ausgabe 4/2015

Energy Systems 4/2015 Zur Ausgabe

Original Paper

International experience with compressed natural gas (CNG) as environmental friendly fuel

Original Paper

Electrical distribution network operation with a presence of distributed generation units in a Pre Smart Grid environment using a clustering-based methodology

Original Paper

Pilot field test of intentional islanding in distribution network

Original Paper

A simulation model with multi-attribute utility functions for energy consumption scheduling in a smart grid

Original Paper

Enhancement of microgrid dynamic responses under fault conditions using artificial neural network for fast changes of photovoltaic radiation and FLC for wind turbine