多维时间序列的建模及预报

Modeling and Forecasting of Multivariate Time Series

摘要: 本文针对多维时序传函（TF）模型的等效规范型，提出了由初建模和推广的Kalman自适应估计预报（EKAEP）算法组成的建模预报方案．考虑到实际系统的时变性和存在不良数据，EKAEP还包括噪声统计特性有限记忆自适应估计器（LMAE）和双置信区间的不良数据检测方法．仿真结果验证了方案的有效性．

Abstract: For an equivalent canonical form of multivariate time series transfer function model,a modeling and forecasting scheme,which consists of preliminary modeling and Extended Kalman Adaptive Estimation-Prediction (EKAEP) algorithm,is presented in this paper.In consideration of time-variation and outliers of physical systems,both the noise statistic property Limited Memory Adaptive Estimator (IMAE) and the outlier detection method based on the criterion of doube believable intervals are included in the EKAEP algorithm.The efficiency of the scheme has been proved by several simulations.

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