Detection and estimation of additive outliers in seasonal time series

The detection of outliers in a time series is an important issue because their presence may have serious negative effects on the analysis in many different ways. Moreover the presence of a complex seasonal pattern in the series could affect the properties of the usual outlier detection procedures. T...

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Bibliographic Details
Published inComputational statistics Vol. 35; no. 3; pp. 1393 - 1409
Main Authors Battaglia, Francesco, Cucina, Domenico, Rizzo, Manuel
Format Journal Article
LanguageEnglish
Published Berlin/Heidelberg Springer Berlin Heidelberg 01.09.2020
Subjects
Economic Theory/Quantitative Economics/Mathematical Methods
Mathematics and Statistics
Original Paper
Probability and Statistics in Computer Science
Probability Theory and Stochastic Processes
Statistics
False detection
Periodic autoregressive process
Periodic autocorrelation
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Summary:The detection of outliers in a time series is an important issue because their presence may have serious negative effects on the analysis in many different ways. Moreover the presence of a complex seasonal pattern in the series could affect the properties of the usual outlier detection procedures. Therefore modelling the appropriate form of seasonality is a very important step when outliers are present in a seasonal time series. In this paper we present some procedures for detection and estimation of additive outliers when parametric seasonal models, in particular periodic autoregressive, are specified to fit the data. A simulation study is presented to evaluate the benefits and the drawbacks of the proposed procedure on a selection of seasonal time series. An application to three real time series is also examined.
ISSN:0943-4062
1613-9658
DOI:10.1007/s00180-019-00928-5