Publication type: Conference paper
Type of review: Peer review (abstract)
Title: Detection of compatible turning-points and signal-extraction of non-stationary time series
Authors: Wildi, Marc
DOI: 10.1007/978-3-642-58409-1_29
Proceedings: Operations Research Proceedings 1998
Page(s): 293
Pages to: 299
Conference details: International Conference on Operations Research, Zurich, 31 August - 3 September 1998
Issue Date: 1999
Series: Operations Research Proceedings
Series volume: 1998
Publisher / Ed. Institution: Springer
Publisher / Ed. Institution: Heidelberg
ISBN: 978-3-540-65381-3
Language: English
Subject (DDC): 510: Mathematics
Abstract: Traditionally, trend-estimation or -extraction methods for non-stationary time series (ARIMA-model based, structural models, Census X12,…) make use of a stochastic modeling procedure which not only determines an optimal symmetric MA(∞)-extraction filter (this is not true for Census XI2) but also supplies missing values at both ends of a finite sample by optimal fore- and backcasts, hence minimizing the unconditional final revision variance. In this paper we propose a new trend estimation procedure based on a direct filtering approach. We generalize the class of time invariant filters by including explicit time dependence towards the end of a sample and optimizing in each time point a corresponding filter with respect to a conditional final revision variance minimization. The condition corresponds to a time delay restriction and this will generalize usual unconditional optimization procedures. It is shown that this optimization underlies an uncertainty-principle (APUP) which is best solved by general IIR- or ARMA-filters instead of the usual MA-designs. This direct IIR-filter-method may be used either for traditional trend extraction or for detection of compatible turning-points of a series (to be defined below). In the latter case it is shown that the theoretical extraction filter has a transferfunction taking the form of an indicator function.
Fulltext version: Published version
License (according to publishing contract): Licence according to publishing contract
Departement: School of Engineering
Organisational Unit: Institute of Data Analysis and Process Design (IDP)
Appears in collections:Publikationen School of Engineering

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