基于正交最小二乘估计的非线性时间序列的预测

在对非线性时间序列的短期预测中经常采用局部线性化的预测算法 ,原有的算法使用普通最小二乘法 (LS)估计近似线性模型的参数。对于存在噪声的数据 ,该算法的数值稳定性较差。本文在对非线性空间进行局部线性化的基础上 ,采用正交最小二乘方法 (OLS)对线性模型同时进行结构选择与参数辨识 ,改善了数值的病态特性 ,增强了算法的稳定性

Nonlinear Time Series Prediction Based on Orthogonal Least Squares Algorithm

Local linear prediction is often applied to predict nonlinear time series, which uses the ordinary least square(LS) method to estimate the parameters in the approximated linear models If there exists noise in the process, the computational stability of the method is rather poor This paper presents an improved method that uses the orthogonal least square (OLS) algorithm to estimate both the structure and the parameters in the linear models from linearizing locally the whole nonlinear space The proposed method can solve the ill-posed numerical problem to some extent and increase the stability of prediction algorithm