The Research Of The Application About HHT And AC Algorithm In Financial Series Analysis

The traditional frequency analysis method often subject to Heisenberg uncertainty principle when processing the non-stationary time series, so it is unable to achieve high precision in both time and frequency simultaneously; 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.HHT(Hilbert-Huang transform)is a new data analysis method proposed by N. E. Huang in 1998, it consists of two steps: EMD(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, and the IMFs which introduced by EMD has the physics significance instantaneous frequency, it can therefore overcome the conventional route's insufficiency which in processing the financial time series which has greatly strengthened non-stationary characteristic.The complicated system like stock market, the meteorological system and so on, the correlation between system variables is often very complex, it can hardly find out the regularity of them, the ultimate characteristic is: the time series have the greatly strengthened misalignment, the non-stationary. Therefore, the traditional qualitative forecast technique, including the Delphi law and the goal decomposition forecast law, the traditional time sequence model, including the running mean model and the index smoothing procedures, the traditional causes and effects model, including decomposition models, forecast law, ARIMA, regression analysis isn't suitable for this kind of system's modeling. But non-parameter, self organization data mining algorithm-- analogy compose algorithm (AC algorithm) 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.