| 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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Wildi, M. (1999). Detection of compatible turning-points and signal-extraction of non-stationary time series [Conference paper]. Operations Research Proceedings 1998, 293–299. https://doi.org/10.1007/978-3-642-58409-1_29
Wildi, M. (1999) ‘Detection of compatible turning-points and signal-extraction of non-stationary time series’, in Operations Research Proceedings 1998. Heidelberg: Springer, pp. 293–299. Available at: https://doi.org/10.1007/978-3-642-58409-1_29.
M. Wildi, “Detection of compatible turning-points and signal-extraction of non-stationary time series,” in Operations Research Proceedings 1998, 1999, pp. 293–299. doi: 10.1007/978-3-642-58409-1_29.
WILDI, Marc, 1999. Detection of compatible turning-points and signal-extraction of non-stationary time series. In: Operations Research Proceedings 1998. Conference paper. Heidelberg: Springer. 1999. S. 293–299. ISBN 978-3-540-65381-3
Wildi, Marc. 1999. “Detection of Compatible Turning-Points and Signal-Extraction of Non-Stationary Time Series.” Conference paper. In Operations Research Proceedings 1998, 293–99. Heidelberg: Springer. https://doi.org/10.1007/978-3-642-58409-1_29.
Wildi, Marc. “Detection of Compatible Turning-Points and Signal-Extraction of Non-Stationary Time Series.” Operations Research Proceedings 1998, Springer, 1999, pp. 293–99, https://doi.org/10.1007/978-3-642-58409-1_29.
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