<div class="csl-bib-body">
<div class="csl-entry">Fischer, G. (2020). <i>Blind source separation for compositional time series</i> [Diploma Thesis, Technische Universität Wien]. reposiTUm. https://doi.org/10.34726/hss.2020.61440</div>
</div>
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dc.identifier.uri
https://doi.org/10.34726/hss.2020.61440
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dc.identifier.uri
http://hdl.handle.net/20.500.12708/15034
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dc.description
Abweichender Titel nach Übersetzung der Verfasserin/des Verfassers
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dc.description.abstract
This thesis shows how blind source separation methods for time-series can be applied to compositional time series. In many applications data sets are of compositional nature, meaning that the relative values of the variables are of interest instead of the absolute ones. Blind source separation (BSS) is a popular modelling approach for multivariate time-series, since it aims to decompose them into latent sources on which univariate modelling is possible. Compositional time-series are per definition multivariate. Moreover, in their isometric-log-ratio-coordinate representation, on which the BSS models are built, they are multivariate if the number of compositions is greater than two. Therefore blind source separation is very useful for compositional time-series. Our methodology is illustrated on a real world data set: Absorption data from a stream in Lower Austria. In the study of dissolved organic matter, ratios of absorption coefficients have been used to indicate the quality of dissolved organic matter in various environments, yielding compositional time series data, on which our new method can be applied.
en
dc.language
English
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dc.language.iso
en
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dc.rights.uri
http://rightsstatements.org/vocab/InC/1.0/
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dc.subject
SOBI
de
dc.subject
log-Verhältnis-Transformation
de
dc.subject
Verhältnisse von Absorptionskoeffizienten
de
dc.subject
SOBI
en
dc.subject
log-ratio-transformation
en
dc.subject
absorption ratios
en
dc.title
Blind source separation for compositional time series
en
dc.title.alternative
Blind Source Separation für kompositionelle Zeitreihen
de
dc.type
Thesis
en
dc.type
Hochschulschrift
de
dc.rights.license
In Copyright
en
dc.rights.license
Urheberrechtsschutz
de
dc.identifier.doi
10.34726/hss.2020.61440
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dc.contributor.affiliation
TU Wien, Österreich
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dc.rights.holder
Gregor Fischer
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dc.publisher.place
Wien
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tuw.version
vor
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tuw.thesisinformation
Technische Universität Wien
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tuw.publication.orgunit
E105 - Institut für Stochastik und Wirtschaftsmathematik
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dc.type.qualificationlevel
Diploma
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dc.identifier.libraryid
AC15673216
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dc.description.numberOfPages
67
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dc.thesistype
Diplomarbeit
de
dc.thesistype
Diploma Thesis
en
dc.rights.identifier
In Copyright
en
dc.rights.identifier
Urheberrechtsschutz
de
tuw.advisor.staffStatus
staff
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tuw.advisor.orcid
0000-0002-3758-8501
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item.languageiso639-1
en
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item.fulltext
with Fulltext
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item.openaccessfulltext
Open Access
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item.mimetype
application/pdf
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item.openairetype
master thesis
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item.grantfulltext
open
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item.openairecristype
http://purl.org/coar/resource_type/c_bdcc
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item.cerifentitytype
Publications
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crisitem.author.dept
E105 - Institut für Stochastik und Wirtschaftsmathematik