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- W2019155478 abstract "A method for identifying and estimating outliers in a time series is proposed, based on fitting functional autoregressive models. Both additive and innovation outliers may be defined. A simulation experiment and the analysis of some real data sets suggest that the proposed method is effective both for series following some nonlinear models, such as self-exciting threshold autoregressive or exponential autoregressive, and for linear series generated by autoregressive moving average processes." @default.
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- W2019155478 title "Outliers in functional autoregressive time series" @default.
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- W2019155478 doi "https://doi.org/10.1016/j.spl.2005.02.003" @default.
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