An Application of Nonlinear Time Series Forecasting
通过实际案例展示，当数据存在非线性特征时，如何利用残差平方的自相关函数检测非线性，并用双线性模型改进ARIMA预测，且结果对模型设定变化较为稳健。
Abstract By means of a real application, it is seen how ARIMA forecasts can be improved when nonlinearities are present. The autocorrelation function (ACF) of the squared residuals provides a convenient tool to check the linearity assumption. Once nonlinearity has been detected, parsimonious bilinear processes seem rather adequate to model it. The detection of nonlinearity and the forecast improvement appear to be rather robust with respect to changes in the linear and bilinear specification. Finally, what bilinear models seem to capture are periods of atypical behavior or sequences of outliers.