ARIMAX模型中的异常点挖掘  

Outlier Mining in ARIMAX Model

作　　者：吴吟吟[1] 

机构地区：[1]无锡职业技术学院,江苏无锡214121

出　　处：《贵州工业大学学报（自然科学版）》2008年第3期8-11,15,共5页Journal of Guizhou University of Technology(Natural Science Edition)

摘　　要：异常点挖掘的意义主要体现在两个方面。传统观念中,异常点常常被认为是噪声数据或无用数据,分析时的一般方法是排除这些干扰数据,更好地估计模型的参数。然而,随着Lon-Mu Liu.et(2001)在快餐行业的数据中进行了实例分析,异常点挖掘也被用于挖掘异常点本身所蕴含的信息。ARIMAX模型引入了外部变量,可以更好地拟合数据。因而对含异常点的ARIMAX模型,提出了利用Gibbs抽样挖掘其中AO型异常点的方法,最后进行了模拟试验,取得了较好的结果。Outlier mining can be used in two sides. In conventional concept,outlier was often presumed to be noise or useless data and was removed in analysis. At present,Lon - Mu Liu etc （2001） conducted an illustration using fast- food restaurant franchise data, which considered the commercial value in oudier data indeed. The autoregressive moving average with exogenous variable （ARIMAX） model is more complete than AR or ARMA model. In mining outliers based on ARIMAX models with AO outliers, this paper proposes Gibbs sampling methods to mine outlier in the view of Bayesian. At last, simulation is done by computer, we obtain a better result.

关 键 词：ARIMAX模型 异常点 GIBBS抽样 

分 类 号：O212.2[理学—概率论与数理统计]