Detection of Additive Outliers in Poisson INAR(1) Time Series

2015 ◽  
pp. 377-388 ◽  
Author(s):  
Maria Eduarda Silva ◽  
Isabel Pereira
Keyword(s):  
Time Series ◽  
Additive Outliers

scholarly journals SEARCHING FOR ADDITIVE OUTLIERS IN NONSTATIONARY TIME SERIES*

2003 ◽  
Vol 24 (2) ◽  
pp. 193-220 ◽  
Author(s):  
Pierre Perron ◽  
Gabriel Rodríguez
Keyword(s):  
Time Series ◽  
Nonstationary Time Series ◽  
Additive Outliers

scholarly journals Detection of Additive Outliers in Seasonal Time Series

2009 ◽  
Author(s):  
Niels Haldrup ◽  
Andreu Sansó ◽  
Antonio Montañés
Keyword(s):  
Time Series ◽  
Seasonal Time Series ◽  
Additive Outliers

scholarly journals Testing for Additive Outliers in Seasonally Integrated Time Series

2004 ◽  
Author(s):  
Niels Haldrup ◽  
Antonio Montañes ◽  
Andreu Sansó
Keyword(s):  
Time Series ◽  
Additive Outliers ◽  
Integrated Time Series

scholarly journals Detection of Outliers and Patches in Bilinear Time Series Models

2010 ◽  
Vol 2010 ◽  
pp. 1-10 ◽  
Author(s):  
Ping Chen ◽  
Ling Li ◽  
Ye Liu ◽  
Jin-Guan Lin
Keyword(s):  
Time Series ◽  
Monte Carlo ◽  
Gibbs Sampling ◽  
Monte Carlo Methods ◽  
Time Series Models ◽  
Posterior Distributions ◽  
Sampling Algorithm ◽  
Additive Outliers ◽  
Gibbs Sampling Algorithm ◽  
Detection Of Outliers

We propose a Gibbs sampling algorithm to detect additive outliers and patches of outliers in bilinear time series models based on Bayesian view. We first derive the conditional posterior distributions, and then use the results of first Gibbs run to start the second adaptive Gibbs sampling. It is shown that our procedure could reduce possible effects on masking and swamping. At last, some simulations are performed to demonstrate the efficacy of detection and estimation by Monte Carlo methods.

scholarly journals Detection of Additive Outliers in Seasonal Time Series

2011 ◽  
Vol 3 (2) ◽  
Author(s):  
Niels Haldrup ◽  
Antonio Montañes ◽  
Andreu Sansó
Keyword(s):  
Time Series ◽  
Seasonal Time Series ◽  
Additive Outliers

A bootstrap test for additive outliers in non-stationary time series

2013 ◽  
Vol 34 (4) ◽  
pp. 454-465 ◽  
Author(s):  
Sam Astill ◽  
David I. Harvey ◽  
A. M. Robert Taylor
Keyword(s):  
Time Series ◽  
Stationary Time Series ◽  
Bootstrap Test ◽  
Stationary Time ◽  
Additive Outliers

scholarly journals Detection and estimation of additive outliers in seasonal time series

2019 ◽  
Vol 35 (3) ◽  
pp. 1393-1409
Author(s):  
Francesco Battaglia ◽  
Domenico Cucina ◽  
Manuel Rizzo
Keyword(s):  
Time Series ◽  
Detection And Estimation ◽  
Seasonal Time Series ◽  
Additive Outliers

scholarly journals  On the Performance of Garch Family Models in the Presence of Additive Outliers

2018 ◽  
Author(s):  
Monday Osagie Adenomon ◽  
Ngozi G. Emenogu ◽  
Nweze Nwaze Obinna
Keyword(s):  
Time Series ◽  
Mean Absolute Error ◽  
Financial Time Series ◽  
Absolute Error ◽  
Medium Size ◽  
Series Length ◽  
Financial Time ◽  
Large Size ◽  
Additive Outliers ◽  
Family Models

It is a common practice to detect outliers in a financial time series in order to avoid the adverse effect of additive outliers. This paper investigated the performance of GARCH family models (sGARCH; gjrGARCH; iGARCH; TGARCH and NGARCH) in the presence of different sizes of outliers (small, medium and large) for different time series lengths (250, 500, 750, 1000, 1250 and 1500)  using root mean square error (RMSE) and mean absolute error (MAE) to adjudge the models. In a simulation iteration of 1000 times in R environment using rugarch package, results revealed that for small size of outliers, irrespective of the length of time series, iGARCH dominated, for medium size of outliers, it was sGARCH and gjrGARCH that dominated irrespective of time series length, while for large size of outliers, irrespective of time series length, gjrGARCH dominated. The study further leveled that in the presence of additive outliers on time series analysis, both RMSE and MAE increased as the time series length increased.

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