A generalized pattern matching approach for multi-step prediction of crude oil price
AbstractThis 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.
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Bibliographic InfoArticle provided by Elsevier in its journal Energy Economics.
Volume (Year): 30 (2008)
Issue (Month): 3 (May)
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- 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.
- 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.
- 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.
- Robinson, Peter M. & Yajima, Yoshihiro, 2002.
"Determination of cointegrating rank in fractional systems,"
Journal of Econometrics,
Elsevier, vol. 106(2), pages 217-241, February.
- Peter M Robinson & Yoshihiro Yajima, 2001. "Determination of Cointegrating Rank in Fractional Systems," STICERD - Econometrics Paper Series /2001/423, Suntory and Toyota International Centres for Economics and Related Disciplines, LSE.
- Sadorsky, Perry, 2006. "Modeling and forecasting petroleum futures volatility," Energy Economics, Elsevier, vol. 28(4), pages 467-488, July.
- 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.
- 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.
- 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.
- 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.
- Abramson, Bruce & Finizza, Anthony, 1991. "Using belief networks to forecast oil prices," International Journal of Forecasting, Elsevier, vol. 7(3), pages 299-315, November.
- Michael Ye & John Zyren & Joanne Shore, 2006. "Short-Run Crude Oil Price and Surplus Production Capacity," International Advances in Economic Research, Springer, vol. 12(3), pages 390-394, August.
- 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.
- Panas, Epaminondas & Ninni, Vassilia, 2000. "Are oil markets chaotic? A non-linear dynamic analysis," Energy Economics, Elsevier, vol. 22(5), pages 549-568, October.
- 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.
- Wang, Tao & Yang, Jian, 2010. "Nonlinearity and intraday efficiency tests on energy futures markets," Energy Economics, Elsevier, vol. 32(2), pages 496-503, March.
- Cheong, Chin Wen, 2009. "Modeling and forecasting crude oil markets using ARCH-type models," Energy Policy, Elsevier, vol. 37(6), pages 2346-2355, June.
- 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.
- Trapero, Juan R. & Pedregal, Diego J., 2009. "Frequency domain methods applied to forecasting electricity markets," Energy Economics, Elsevier, vol. 31(5), pages 727-735, September.
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