Time series analysis in self-regulating systems. Abstract

Time series analysis in self-regulating systems

V. F. Fedorov

Moscow Institute of Physics and Technology (State University), 9 Institutsky Per., Dolgoprudny, Moscow region 141700, Russia

Peoples' Friendship University of Russia, Miklukho-Maklaya str. 6, Moscow 117198, Russia

The paper is received on April 23, 2019

Abstract. This paper describes a new approach to time series analysis of quantitative parameters of functional diagnostics signals. The normalization of measured values is introduced, which reduces the influence of individual differences when comparing data of different patients In addition, the normalization allows to build classification rules based on multivariate statistics and draw distinctions between heterogeneous groups more precisely. The effectiveness of normalization algorithms tested on the time series of durations of cardiac cycles, systolic output and peripheral vascular resistance. We suggest using this approach for time series analysis of quantitative parameters of other self-regulating systems.

Keywords: self-regulating systems; time series analysis; functional diagnostics.

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V.F. Fedorov. Time series analysis in self-regulating systems. Zhurnal Radioelektroniki - Journal of Radio Electronics. 2019. No.5. Available at http://jre.cplire.ru/jre/may19/9/text.pdf