Abstract Advantages and limitations of the existing models for practical forecasting of stock market volatility have been identified. Support vector machine (SVM) have been proposed as a complimentary volatility model that is capable to extract i

Including the least squares method, the SVM, neural networks, 1 _k neighbor method, The received signal is given eye and BER simulation systems, Principal component analysis of multivariate data analysis projection.

Including the least squares method, the SVM, neural networks, 1 _k neighbor method, Can realize the two-dimensional data clustering, wolf calculated Lyapunov exponent.