混沌序列自适应多步预测及在股票中的应用

针对混沌时间序列自适应预测方法在多步预测中预测器系数无法调节的问题,根据混沌时间序列的短期可预测性及自适应算法的自适应跟踪混沌运动轨迹的特点,提出了一种自适应多步预测方法.在多步预测中,该方法根据已知样本得到对将来值的预测值并能自适应调节滤波器系数.仿真结果表明此方法的多步预测性能明显好于自适应预测方法的多步预测性能.将此方法应用于对股票数据的预测,得到了较好的预测结果.

A Novel Adaptive Multi-step-prediction Method for Chaotic Time Series and Its Applications in Stock Market

Based on the short-term predictability of chaotic time series and the adaptive tracking chaotic trajectory of adaptive algorithm,a novel adaptive multi-step-prediction method is proposed to resolve the problem of adjusting filter's parameters of the adaptive prediction method during multi-step prediction in this paper;and this method is used to predict typical chaotic time series and chaotic stock data.During multi-step prediction this method can adjust filter's parameters and get the multi-step prediction value using the data we know.Simulation results show that: this adaptive multi-step-prediction method can be successfully used to make multi-step predictions of typical chaotic time series and stock data,and this method's multi-step-prediction performance is better than the adaptive prediction method's.