A multiple adaptive wavelet recurrent neural network model to analyze crude oil prices
AbstractInternational crude oil prices are an important part of the economy, and trends in changing oil prices have an effect on financial markets. Traditional hybrid analysis methods for international crude oil prices, such as wavelet transform and back propagation neural network (BPNN), seek synergy effects by sequentially filtering data through different models. However, these estimation methods cause loss of information through the introduction of biases in each filtering step, which are aggregated throughout the process when model assumptions are violated, and the traditional BPNN model does not have forecasting ability. In this study, we constructed a multiple wavelet recurrent neural network (MWRNN) simulation model, in which trend and random components of crude oil and gold prices were considered. The wavelet analysis was utilized to capture multiscale data characteristics, while a real neural network (RNN) was utilized to forecast crude oil prices at different scales. Finally, a standard BPNN was added to combine these independent forecasts from different scales into an optimal prediction of crude oil prices. The simulation results showed that the model has high prediction accuracy. The designed neural network is able to predict oil prices with an average error of 4.06% for testing and 3.88% for training data. This forecasting model would be able to predict the world crude oil prices with any commercial energy source prices instead of the gold prices.
Download InfoIf you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
Bibliographic InfoArticle provided by Elsevier in its journal Journal of Economics and Business.
Volume (Year): 64 (2012)
Issue (Month): 4 ()
Contact details of provider:
Web page: http://www.elsevier.com/locate/jeconbus
Multiple wavelet recurrent neural network; Crude oil price forecasting; Gold price;
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- Brown, Stephen P. A. & Yucel, Mine K., 2002.
"Energy prices and aggregate economic activity: an interpretative survey,"
The Quarterly Review of Economics and Finance,
Elsevier, vol. 42(2), pages 193-208.
- Stephen P. A. Brown & Mine K. Yücel, 2001. "Energy prices and aggregate economic activity: an interpretive survey," Working Papers 0102, Federal Reserve Bank of Dallas.
- Morana, Claudio, 2001. "A semiparametric approach to short-term oil price forecasting," Energy Economics, Elsevier, vol. 23(3), pages 325-338, May.
- Paul Stevens, 2005. "Oil Markets," Oxford Review of Economic Policy, Oxford University Press, vol. 21(1), pages 19-42, Spring.
- Abramson, Bruce & Finizza, Anthony, 1991. "Using belief networks to forecast oil prices," International Journal of Forecasting, Elsevier, vol. 7(3), pages 299-315, November.
- 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.
- Donald W. Jones, Paul N. Leiby and Inja K. Paik, 2004. "Oil Price Shocks and the Macroeconomy: What Has Been Learned Since 1996," The Energy Journal, International Association for Energy Economics, vol. 0(Number 2), pages 1-32.
- Mills, Terence C., 2004. "Statistical analysis of daily gold price data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 338(3), pages 559-566.
If references are entirely missing, you can add them using this form.