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Blind Source Separation for Compositional Time Series.
Nordhausen, Klaus; Fischer, Gregor; Filzmoser, Peter.
Afiliación
  • Nordhausen K; CSTAT - Computational Statistics Institute of Statistics and Mathematical Methods in Economics, Vienna University of Technology, Wiedner Hauptstr. 7, 1040 Vienna, Austria.
  • Fischer G; CSTAT - Computational Statistics Institute of Statistics and Mathematical Methods in Economics, Vienna University of Technology, Wiedner Hauptstr. 8-10, 1040 Vienna, Austria.
  • Filzmoser P; CSTAT - Computational Statistics Institute of Statistics and Mathematical Methods in Economics, Vienna University of Technology, Wiedner Hauptstr. 8-10, 1040 Vienna, Austria.
Math Geosci ; 53(5): 905-924, 2021.
Article en En | MEDLINE | ID: mdl-34721726
ABSTRACT
Many geological phenomena are regularly measured over time to follow developments and changes. For many of these phenomena, the absolute values are not of interest, but rather the relative information, which means that the data are compositional time series. Thus, the serial nature and the compositional geometry should be considered when analyzing the data. Multivariate time series are already challenging, especially if they are higher dimensional, and latent variable models are a popular way to deal with this kind of data. Blind source separation techniques are well-established latent factor models for time series, with many variants covering quite different time series models. Here, several such methods and their assumptions are reviewed, and it is shown how they can be applied to high-dimensional compositional time series. Also, a novel blind source separation method is suggested which is quite flexible regarding the assumptions of the latent time series. The methodology is illustrated using simulations and in an application to light absorbance data from water samples taken from a small stream in Lower Austria.
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Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Tipo de estudio: Prognostic_studies Idioma: En Revista: Math Geosci Año: 2021 Tipo del documento: Article País de afiliación: Austria

Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Tipo de estudio: Prognostic_studies Idioma: En Revista: Math Geosci Año: 2021 Tipo del documento: Article País de afiliación: Austria