Use of canonical analysis in time series model identification
利用时间序列模型的二阶矩结构推导典型分析，证明典型相关和特征向量的一致性，提出基于典型相关的ARMA模型阶数确定方法，适用于非平稳和平稳过程，并给出识别统计量的渐近分布。
The second-order moment structure of time series models is used to derive a canonical analysis in time series modelling. Consistency properties of certain canonical correlations and the corresponding eigenvectors are shown. Based on these properties, a canonical correlation approach for tentative order determination in building autoregressive-moving average models is proposed. This approach can handle directly nonstationary as well as stationary processes and it also provides consistent estimates of the auto-regressive parameters involved. The asymptotic distribution of the identification statistic is discussed.