Abstracts

Mathematical modelling

Set of one-dimensional time series segments of length $M$ may be consider as a set of points in $M$-dimensional phase space. Series trajectory in this space -- multidimensional phase portrait -- is a consecutive join of this points, for example, by splines. The principal components method is suitable for reduction of dimension. It consist in founding of coordinate axes, on which the series trajectory projection variance is maximal. Maximization of autocovariance instead of variance lead to the smooth components method. Both methods are applicable to any time series without stationary requirement and very useful for analysis and prediction of animal population structure and density dynamics and influencing factors. Results of this methods application to root vole population density and structure dynamics in Mountain Altai are presented.

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©2001, Siberian Branch of Russian Academy of Science, Novosibirsk
©2001, United Institute of Computer Science SB RAS, Novosibirsk
©2001, Institute of Computational Techologies SB RAS, Novosibirsk
©2001, A.P. Ershov Institute of Informatics Systems SB RAS, Novosibirsk
©2001, Institute of Mathematics SB RAS, Novosibirsk
©2001, Institute of Cytology and Genetics SB RAS, Novosibirsk
©2001, Institute of Computational Mathematics and Mathematical Geophysics SB RAS, Novosibirsk
©2001, Novosibirsk State University
Last modified 06-Jul-2012 (11:45:21)