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Detecting Possibly Non-Consecutive Outliers in Industrial Time Series

Alberto Luceno
Journal of the Royal Statistical Society. Series B (Statistical Methodology)
Vol. 60, No. 2 (1998), pp. 295-310
Published by: Wiley for the Royal Statistical Society
Stable URL: http://www.jstor.org/stable/2985941
Page Count: 16
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Since scans are not currently available to screen readers, please contact JSTOR User Support for access. We'll provide a PDF copy for your screen reader.
Detecting Possibly Non-Consecutive Outliers in Industrial Time Series
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Abstract

A method for robust estimation and multiple outlier detection in time series generated by autoregressive integrated moving average processes in industrial environments is developed. The procedure is based on reweighted maximum likelihood estimation using Huber or redescending weights and, therefore, generalizes the well-established robust M-estimation procedures used in the regression framework. When the scalar process is non-stationary, the computations required can be performed equally well using either the original undifferenced series or auxiliary differenced series. Whereas the latter alternative may be preferred for scalar series, the former might be extended to cope with vector partially non-stationary time series without differencing the series, thus avoiding non-invertibility and parameter identifiability problems caused by overdifferencing. The overall strategy is applied in two real industrial data sets.

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