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Detection of Outlier in Time Series Count Data

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Advances in Time Series Analysis and Forecasting (ITISE 2016)

Part of the book series: Contributions to Statistics ((CONTRIB.STAT.))

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Abstract

Outlier detection for time series data is a fundamental issue in time series analysis. In this work we develop statistical methods in order to detect outliers in time series of counts. More specifically we are interesting on detection of an Innovation Outlier (IO). Models for time series count data were originally proposed by Zeger (Biometrika 75(4):621–629, 1988) [28] and have subsequently generalized into GARMA family. The Maximum Likelihood Estimators of the parameters are discussed and the procedure of detecting an outlier is described. Finally, the proposed method is applied to a real data set.

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Author information

Authors and Affiliations

  1. Department of Accounting, Technological Educational Institution of Western Greece (Patras), Patras, Greece

    Vassiliki Karioti

  2. Department of Civil Engineering, University of Patras, Patras, Greece

    Polychronis Economou

Authors
  1. Vassiliki Karioti
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  2. Polychronis Economou
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Corresponding author

Correspondence to Vassiliki Karioti .

Editor information

Editors and Affiliations

  1. CITIC-UGR, University of Granada, Granada, Spain

    Ignacio Rojas

  2. CITIC-UGR, University of Granada, Granada, Spain

    Héctor Pomares

  3. CITIC-UGR, University of Granada, Granada, Spain

    Olga Valenzuela

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Karioti, V., Economou, P. (2017). Detection of Outlier in Time Series Count Data. In: Rojas, I., Pomares, H., Valenzuela, O. (eds) Advances in Time Series Analysis and Forecasting. ITISE 2016. Contributions to Statistics. Springer, Cham. https://doi.org/10.1007/978-3-319-55789-2_15

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