Estimation and Detection of Outliers and Patches in ARMAX Time Series Models

Abstract

In this paper,the usual Gibbs sampler and adaptive Gibbs sampler are used in the Bayesian analysis of autoregressive moving average with exogenous variable(ARMAX) time series models.Firstly,some methods are used to delete the influence of input process in ARMAX model,and then the method of mining outliers and patches in time series based on the former work is given.It is shown that the adaptive Gibbs sampler is also useful in handling additive isolated outliers and outlier patches in ARMAX model.Practical and simulation studies also show that the procedure can reduce possible masking and swamping effects,and hence improve the existing methods.