Multivariate time series segmentation for generalized description of dynamic systems operation

Authors:
O. A. Mishulina;I. N. Sukonkin
Affiliations:
National Research Nuclear University "MEPhI", Moscow, Russia;National Research Nuclear University "MEPhI", Moscow, Russia
Venue:
Optical Memory and Neural Networks
Year:
2012

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Abstract

In this paper a class of systems that can be in several typical operation modes is considered. The goal of the research is to build a generalized description of such systems. The description is understood as segmentation of multivariate time series of experimental data and identification of typical states models. Two approaches to solving the problem are proposed. The first one is based on evolution models, and the second builds segments using some simple elements. The algorithms are tested on simulated data and applied to describe the behavior of animals in biological experiments.