时间序列异常值探测的Bayes方法及其在电离层VTEC数据处理中的应用

Bayesian Methods for Time Series Outliers Detection and Applications in Ionospheric VTEC Data Processing

  • 摘要: 基于Bayes统计推断理论,提出了自回归模型中异常值定位的Bayes方法;在正态-Gamma先验分布下,分别基于均值漂移模型和方差膨胀模型,提出了后验概率的计算方法,并运用Bayes方法估计了异常扰动;最后将该方法应用到电离层VTEC数据处理的建模中,比较模型修正前后预报的结果,验证了新方法的有效性。

     

    Abstract: The observation of time series may be influenced by outliers.If we forecasts directly,neglecting the influence,will lead to false result.So,it is important to find proper outliers detection method.Based on the theory of Bayesian statistical inference,in this paper we put forward Bayesian method of positioning outliers in autoregressive model firstly;and then,under the condition of normal-gamma prior information,we put forward computation method of posterior probability based on mean shift model and variance inflation model respectively,and estimate the outliers with Bayesian method;at last,the method is applied in the research on the data modeling of ionospheric VTEC series,compared with the forecasting results on unmodified and modified to test the efficiency.

     

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