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Title:

Estimating invertible functional time series

Document type:
Buchbeitrag
Author(s):
Aue, A. and Klepsch, J.
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:
Springer
Semester:
SS 17
TUM Institution:
Lehrstuhl für Mathematische Statistik
Format:
Text
 BibTeX