Abstract
AbstractThe autocorrelation function (acf) and the partial autocorrelation function (pacf) are elementary tools of linear time series analysis. The sensitivity of the conventional sample acf and pacf to outliers is well known. We review robust estimators and evaluate their performances in different data situations considering Gaussian scenarios with and without outliers as well as times series with heavy tails in a simulation study.WIREs Comput Stat2015, 7:205–222. doi: 10.1002/wics.1351This article is categorized under:Statistical and Graphical Methods of Data Analysis > Robust MethodsData: Types and Structure > Time Series, Stochastic Processes, and Functional Data
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