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: | 978-3-540-65381-3 978-3-642-58409-1 |
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. |
URI: | https://digitalcollection.zhaw.ch/handle/11475/16836 |
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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