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Sliced inverse regression for high-dimensional time series

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  • Becker, Claudia
  • Fried, Roland

Abstract

Methods of dimension reduction are very helpful and almost a necessity if we want to analyze high-dimensional time series since otherwise modelling affords many parameters because of interactions at various time-lags. We use a dynamic version of Sliced Inverse Regression (SIR; Li (1991)), which was developed to reduce the dimension of the regressor in regression problems, as an exploratory tool for analyzing multivariate time series. Analyzing each variable individually, we search for those directions, i.e., linear combinations of past and present observations of the other variables which explain most of the variability of the variable considered. This can also provide information on possible nonlinearities. We apply a dynamic version of SIR to multivariate physiological time series observed in intensive care.

Suggested Citation

  • Becker, Claudia & Fried, Roland, 2001. "Sliced inverse regression for high-dimensional time series," Technical Reports 2001,14, Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen.
    • Handle: RePEc:zbw:sfb475:200114
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    References listed on IDEAS

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    1. Karlsen, Hans Arnfinn & Myklebust, Terje & Tjøstheim, Dag, 2000. "Nonparametric estimation in a nonlinear cointegration type model," SFB 373 Discussion Papers 2000,33, Humboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes.
    2. Gather, Ursula & Fried, Roland & Lanius, Vivian & Imhoff, Michael, 2001. "Online monitoring of high dimensional physiological time series: A case study," Technical Reports 2001,03, Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen.
    3. Peter Molenaar, 1985. "A dynamic factor model for the analysis of multivariate time series," Psychometrika, Springer;The Psychometric Society, vol. 50(2), pages 181-202, June.
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