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2017 | OriginalPaper | Chapter

Detection of Outlier in Time Series Count Data

Authors : Vassiliki Karioti, Polychronis Economou

Published in: Advances in Time Series Analysis and Forecasting

Publisher: 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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Metadata
Title
Detection of Outlier in Time Series Count Data
Authors
Vassiliki Karioti
Polychronis Economou
Copyright Year
2017
Book
Advances in Time Series Analysis and Forecasting

Print ISBN: 978-3-319-55788-5

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

Copyright Year: 2017

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