4Farooq M,Mahdi N.Forecasting output using oil prices:a cascaded artificial neural network approach[J]Journal of Economics and Business,2006,58(2) : 168-180.
5Gori F,Ludovisi D,Cerritelli P F.Forecast of oil price and consumption in the short term under three scenarios:parabolic,linear and chaotic behaviour[J].Energy,2007,32(7):1291-1296.
6Mirmirani S,Li H C.A comparison of VAR and neural networks with genetic algorithm in forecasting price of oil[J].Advances in Econometrics, 2004,19 : 203-223.
7Vapnik V N.The nature of statistical learning theory[M].New York: Springer-Verlag, 1995.
8Suykens J K,Gestel T.Least squares support vector machines[M]. Singapore: World Scientifics, 2002.
9Suykens J K, De Brabanter J,Lukas L.Weighted least squares support vector machines:robustness and sparse approximation[J].Neurocomputing, 2002,48 ( 1 ) : 85-105.
10Takens F. On the numerical determination of the dimension of an attractor[A]. In:Rand D, Young L S.Dynamical Systems and Turbulence, Warwick, 1980,Lecture Notes in Mathematice [M], Springer-Verlag,1981. 898: 316- 381.
2Rahman, m. Mustafa, M. Dynamic. Linkages and Granger Causality between Short - term USC or porate Bondand Stock Markets [ J ]. Applied EconomicsLetters, 1997,4 ( 1 ) : 89 -91.
3Ramin, C. M. Tiong, S. K. A Vector Correction Model of the Singapore Stock Market [ J ]. Intermational Review of Eco- momics and Finance,2000,9 ( 3 ) :79 - 96.