Detection of Outlier Patches in Autoregressive Time Series

This paper proposes a procedure to detect patches of outliers in an autoregressive process. The procedure is an improvement over the existing detection methods via Gibbs sampling. We show that the standard outlier detection via Gibbs sampling may be extremely ine cient in the presence of severe masking and swamping e ects. The new procedure identi es the beginning and end of possible outlier patches using the existing Gibbs sampling, then carries out an adaptive procedure with block interpolation to handle patches of outliers. Empirical and simulated examples show that the proposed procedure is e ective.