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...