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Forecasting energy market indices with recurrent neural networks: Case study of crude oil price fluctuations

Listed author(s):
  • Wang, Jie
  • Wang, Jun
Registered author(s):

    In an attempt to improve the forecasting accuracy of crude oil price fluctuations, a new neural network architecture is established in this work which combines Multilayer perception and ERNN (Elman recurrent neural networks) with stochastic time effective function. ERNN is a time-varying predictive control system and is developed with the ability to keep memory of recent events in order to predict future output. The stochastic time effective function represents that the recent information has a stronger effect for the investors than the old information. With the established model the empirical research has a good performance in testing the predictive effects on four different time series indices. Compared to other models, the present model is possible to evaluate data from 1990s to today with extreme accuracy and speedy. The applied CID (complexity invariant distance) analysis and multiscale CID analysis, are provided as the new useful measures to evaluate a better predicting ability of the proposed model than other traditional models.

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    File URL: http://www.sciencedirect.com/science/article/pii/S0360544216301608
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    Article provided by Elsevier in its journal Energy.

    Volume (Year): 102 (2016)
    Issue (Month): C ()
    Pages: 365-374

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    Handle: RePEc:eee:energy:v:102:y:2016:i:c:p:365-374
    DOI: 10.1016/j.energy.2016.02.098
    Contact details of provider: Web page: http://www.journals.elsevier.com/energy

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    1. Saif Ghouri, Salman, 2006. "Assessment of the relationship between oil prices and US oil stocks," Energy Policy, Elsevier, vol. 34(17), pages 3327-3333, November.
    2. Alvarez-Ramirez, Jose & Alvarez, Jesus & Rodriguez, Eduardo, 2008. "Short-term predictability of crude oil markets: A detrended fluctuation analysis approach," Energy Economics, Elsevier, vol. 30(5), pages 2645-2656, September.
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    6. Wang, Tiansong & Wang, Jun & Zhang, Junhuan & Fang, Wen, 2011. "Voter interacting systems applied to Chinese stock markets," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 81(11), pages 2492-2506.
    7. Yao Yu & Jun Wang, 2012. "Lattice-oriented percolation system applied to volatility behavior of stock market," Journal of Applied Statistics, Taylor & Francis Journals, vol. 39(4), pages 785-797, August.
    8. Olson, Dennis & Mossman, Charles, 2003. "Neural network forecasts of Canadian stock returns using accounting ratios," International Journal of Forecasting, Elsevier, vol. 19(3), pages 453-465.
    9. Ghiassi, M. & Saidane, H. & Zimbra, D.K., 2005. "A dynamic artificial neural network model for forecasting time series events," International Journal of Forecasting, Elsevier, vol. 21(2), pages 341-362.
    10. Xiao, Di & Wang, Jun, 2012. "Modeling stock price dynamics by continuum percolation system and relevant complex systems analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(20), pages 4827-4838.
    11. Ebrahimpour, Reza & Nikoo, Hossein & Masoudnia, Saeed & Yousefi, Mohammad Reza & Ghaemi, Mohammad Sajjad, 2011. "Mixture of MLP-experts for trend forecasting of time series: A case study of the Tehran stock exchange," International Journal of Forecasting, Elsevier, vol. 27(3), pages 804-816, July.
    12. Makridakis, Spyros, 1993. "Accuracy measures: theoretical and practical concerns," International Journal of Forecasting, Elsevier, vol. 9(4), pages 527-529, December.
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