A generalized pattern matching approach for multi-step prediction of crude oil price
This paper applies pattern matching technique to multi-step prediction of crude oil prices and proposes a new approach: generalized pattern matching based on genetic algorithm (GPMGA), which can be used to forecast future crude oil price based on historical observations. This approach can detect the most similar pattern in contemporary crude oil prices from the historical data. Based on the similar historical pattern, a multi-step prediction of future crude oil prices can be figured out. In GPMGA modeling process, the traditional pattern matching is not directly employed. Historical data is transformed to larger or smaller scales in the x-axis and the y-axis directions, so that a generalized price pattern reflecting current price movement can be obtained. This treatment overcomes the local deficiency of the traditional pattern modeling in recognition system approach (PMRS), and in addition to this, a matched historical pattern in a larger pattern size can be found. Since the approach takes not only historical similarities but also differences into account, the concept of "generalized pattern matching" is proposed here. It proves a new basis for multi-step prediction by finding out more essential similarities through various transformations. The related empirical study is constructed for a one-month forecasting of the Brent and WTI crude oil prices, and satisfying forecasting results are attained. At the end, comparisons with some other time series prediction approaches, such as PMRS and Elman network, demonstrate the effectiveness and superiority of GPMGA over others.
If 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.
As the access to this document is restricted, you may want to look for a different version under "Related research" (further below) or search for a different version of it.
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.:
- Ye, Michael & Zyren, John & Shore, Joanne, 2006. "Forecasting short-run crude oil price using high- and low-inventory variables," Energy Policy, Elsevier, vol. 34(17), pages 2736-2743, November.
- Ye, Michael & Zyren, John & Shore, Joanne, 2005. "A monthly crude oil spot price forecasting model using relative inventories," International Journal of Forecasting, Elsevier, vol. 21(3), pages 491-501.
- Moosa, Imad A. & Al-Loughani, Nabeel E., 1994. "Unbiasedness and time varying risk premia in the crude oil futures market," Energy Economics, Elsevier, vol. 16(2), pages 99-105, April.
- Robinson, Peter M. & Yajima, Yoshihiro, 2002. "Determination of cointegrating rank in fractional systems," Journal of Econometrics, Elsevier, vol. 106(2), pages 217-241, February.
- Gil-Alana, Luis A., 2001. "A fractionally integrated model with a mean shift for the US and the UK real oil prices," Economic Modelling, Elsevier, vol. 18(4), pages 643-658, December.
- Gulen, S. Gurcan, 1998. "Efficiency in the crude oil futures market," Journal of Energy Finance & Development, Elsevier, vol. 3(1), pages 13-21.
- Alvarez-Ramirez, Jose & Soriano, Angel & Cisneros, Myriam & Suarez, Rodolfo, 2003. "Symmetry/anti-symmetry phase transitions in crude oil markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 322(C), pages 583-596.
- Zhang, Guoqiang & Eddy Patuwo, B. & Y. Hu, Michael, 1998. "Forecasting with artificial neural networks:: The state of the art," International Journal of Forecasting, Elsevier, vol. 14(1), pages 35-62, March.
- Abramson, Bruce & Finizza, Anthony, 1995. "Probabilistic forecasts from probabilistic models: A case study in the oil market," International Journal of Forecasting, Elsevier, vol. 11(1), pages 63-72, March.
- Alvarez-Ramirez, Jose & Cisneros, Myriam & Ibarra-Valdez, Carlos & Soriano, Angel, 2002. "Multifractal Hurst analysis of crude oil prices," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 313(3), pages 651-670.
- Fong, Wai Mun & See, Kim Hock, 2002. "A Markov switching model of the conditional volatility of crude oil futures prices," Energy Economics, Elsevier, vol. 24(1), pages 71-95, January.
- Bernabe, Araceli & Martina, Esteban & Alvarez-Ramirez, Jose & Ibarra-Valdez, Carlos, 2004. "A multi-model approach for describing crude oil price dynamics," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 338(3), pages 567-584.
- Sadorsky, Perry, 2006. "Modeling and forecasting petroleum futures volatility," Energy Economics, Elsevier, vol. 28(4), pages 467-488, July.
- Abramson, Bruce & Finizza, Anthony, 1991. "Using belief networks to forecast oil prices," International Journal of Forecasting, Elsevier, vol. 7(3), pages 299-315, November.
- Tang, Linghui & Hammoudeh, Shawkat, 2002. "An empirical exploration of the world oil price under the target zone model," Energy Economics, Elsevier, vol. 24(6), pages 577-596, November.
- Adrangi, Bahram & Chatrath, Arjun & Dhanda, Kanwalroop Kathy & Raffiee, Kambiz, 2001. "Chaos in oil prices? Evidence from futures markets," Energy Economics, Elsevier, vol. 23(4), pages 405-425, July.
- repec:kap:iaecre:v:12:y:2006:i:3:p:390-394 is not listed on IDEAS
- Panas, Epaminondas & Ninni, Vassilia, 2000. "Are oil markets chaotic? A non-linear dynamic analysis," Energy Economics, Elsevier, vol. 22(5), pages 549-568, October.