The Application Of HHT And GMDH For Analysis Of Financial Time Series

The stock market system, the correlation between system variables is often very complex, it can hardly find out the regularity of them, Therefore, the traditional qualitative forecast technique isn't suitable for this kind of system's modeling. But self organization data mining algorithm-group method of data handling (GMDH) which carries on the forecast to the output variable does not need to carry on the estimate or the supposition in advance to the input variable trend of development, its forecasting result rests on the completely foregone data, therefore it is very fit for this kind of system's modeling.The traditional time analysis method always based on the stable, normal distribution's, linear hypothesis and so on, but in fact it is often untenable, therefore it is likely to have the serious distortion when cheating some non-stationary time series.Hilbert-Huang transform is a new data analysis method proposed by N. E. Huang in 1998, it consists of two steps: empirical mode decomposition and HAS(Hilbert spectrum analysis). Because this method is totally adaptive; it can process non-stationary data, and not subject to Heisenberg uncertainty principle.