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2017 | OriginalPaper | Buchkapitel

Detection of Outlier in Time Series Count Data

verfasst von : Vassiliki Karioti, Polychronis Economou

Erschienen in: Advances in Time Series Analysis and Forecasting

Verlag: Springer International Publishing

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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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Titel: Detection of Outlier in Time Series Count Data
verfasst von: Vassiliki Karioti
Polychronis Economou
Verlag: Springer International Publishing
Buch: Advances in Time Series Analysis and Forecasting
Print ISBN: 978-3-319-55788-5

Electronic ISBN: 978-3-319-55789-2

Copyright-Jahr: 2017
DOI: https://doi.org/10.1007/978-3-319-55789-2_15

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