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Document type:
Buchbeitrag 
Author(s):
Aue, A. and Klepsch, J. 
Title:
Estimating invertible functional time series 
Pages contribution:
51-58 
Abstract:
This contribution discusses the estimation of an invertible functional time series through fitting of functional moving average processes. The method uses a functional version of the innovations algorithm and dimension reduction onto a number of principal directions. Several methods are suggested to automate the procedures. Empirical evidence is presented in the form of simulations and an application to traffic data. 
Keywords:
Order Selection, Functional Principal Component Analysis, Functional Principal Component, Principal Subspace, Innovation Algorithm 
Editor:
Aneiros, G, Bongiorno, E.G., Cao, R. and Vieu, P. 
Book title:
Functional Statistics and Related Fields. 
Publisher:
Springer 
Date of publication:
28.04.2017 
Year:
2017 
Quarter:
2. Quartal 
Year / month:
2017-04 
Month:
Apr 
Print-ISBN:
978-3319558455 
Language:
en 
WWW:
Semester:
SS 17 
TUM Institution:
Lehrstuhl für Mathematische Statistik 
Format:
Text