滑动窗口二次自回归模型预测混沌时间序列  

Moving Windows Quadratic Autoregressive Model for Predicting Chaotic Time Series

作　　者：李爱国[1] 

机构地区：[1]西安科技大学计算机系,陕西西安710054

出　　处：《系统工程理论与实践》2004年第10期104-109,共6页Systems Engineering-Theory & Practice

基　　金：陕西省科学技术发展计划"十五"攻关资助项目(2000K08-G12)

摘　　要：　提出一种新颖的非线性时间序列预测模型,即滑动窗口二次自回归(MWDAR)模型.MWDAR模型使用部分的历史数据及其二次项构造自回归模型.模型参数用线性最小二乘法估计.应用模型进行预测时,预先选定窗口大小以及模型一次项和二次项的阶次.在每个当前时刻,先根据窗口内的数据估计模型参数,然后根据输入向量及模型参数做出预测.这种预测方法不仅适合小数据集的时间序列预测,而且对大数据集具有极高的计算效率.分别用Henon混沌时间序列数据和真实的股票交易数据作了MWDAR方法与局域线性化方法的1步和多步预测对比实验.结果显示MWDAR方法无论在预测精度上,还是在计算效率上都优于局域线性化方法.A novel model for predicting nonlinear time series is proposed in this paper, namely moving windows quadratic autoregressive (MWDAR) model. The model is constructed by using historical data and the quadratic items of data, and the parameters of the model are estimated by linear least square algorithms. It is necessary to specify the size of the windows, and the orders of the model before prediction process. In every crisp time point, the parameters of the model are estimated according to the data in current window, then, the future value is predicted as a result of the model parameters and the current input vector. The MWDAR model not only works very well on small data sets, but also has high computing efficiency on large data sets. Single and multi-step predictions experiments of comparing the MWDAR model with well-known local linear model are done on synthetic (Henon map) and real data (stock price movements) respectively. The results are excellent: MWDAR model not only has higher precision, but also has higher computing efficiency than local linear model.

关 键 词：非线性系统 混沌时间序列 预测 预报 

分 类 号：TN911.23[电子电信—通信与信息系统]