Bayesian Outlier Detection in Non‐Gaussian Autoregressive Time Series

18 Pages Posted: 29 May 2020

See all articles by Maria Eduarda Silva

Maria Eduarda Silva

Universidade do Porto - Departamento de Matematica Aplicada

Isabel Pereira

affiliation not provided to SSRN

Brendan McCabe

University of Liverpool - Management School (ULMS)

Date Written: September 2019

Abstract

This work investigates outlier detection and modelling in non‐Gaussian autoregressive time series models with margins in the class of a convolution closed parametric family. This framework allows for a wide variety of models for count and positive data types. The article investigates additive outliers which do not enter the dynamics of the process but whose presence may adversely influence statistical inference based on the data. The Bayesian approach proposed here allows one to estimate, at each time point, the probability of an outlier occurrence and its corresponding size thus identifying the observations that require further investigation. The methodology is illustrated using simulated and observed data sets.

Keywords: Convolution closed infinitely divisible models, additive outliers, Bayesian framework, MCMC, time series of counts, state space models

Suggested Citation

Silva, Maria Eduarda and Pereira, Isabel and McCabe, Brendan, Bayesian Outlier Detection in Non‐Gaussian Autoregressive Time Series (September 2019). Journal of Time Series Analysis, Vol. 40, Issue 5, pp. 631-648, 2019, Available at SSRN: https://ssrn.com/abstract=3612250 or http://dx.doi.org/10.1111/jtsa.12439

Maria Eduarda Silva (Contact Author)

Universidade do Porto - Departamento de Matematica Aplicada ( email )

Rua das Taipas 135
4050 600 Porto
Portugal

Isabel Pereira

affiliation not provided to SSRN

No Address Available

Brendan McCabe

University of Liverpool - Management School (ULMS) ( email )

Chatham Street
Liverpool, L69 7ZH
United Kingdom

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