DETECTION OF OUTLIER PATCHES IN
AUTOREGRESSIVE TIME SERIES
Ana Justel, Daniel Peňa and Ruey S. Tsay
Universidad Autónoma de Madrid, Universidad Carlos III de Madrid
Abstract: 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 inefficient in the presence of severe masking and swamping effects. The new procedure identifies 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 effective.
Key words and phrases: Gibbs sampler, multiple outliers, sequential learning, time series.