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.
Outliers in functional auutoregressive time series / Battaglia, Francesco. - In: STATISTICS & PROBABILITY LETTERS. - ISSN 0167-7152. - 72:(2005), pp. 323-332. [10.1016/j.spl.2005.02.003]
Outliers in functional auutoregressive time series
BATTAGLIA, Francesco
2005
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.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
