Disorder indicator for nonstationary stochastic processes
- Authors: Kislitsyn А.A.1, Kozlova A.B.2, Korsakova M.B.2, Orlov Y.N.1
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Affiliations:
- Institute for Applied Mathematics of the Russian Academy of Sciences
- N.N. Burdenko National Scientific and Practical Center for Neurosurgery of the Ministry of Healthcare of the Russian Federation
- Issue: Vol 484, No 4 (2019)
- Pages: 393-396
- Section: Mathematics
- URL: https://journals.eco-vector.com/0869-5652/article/view/12542
- DOI: https://doi.org/10.31857/S0869-56524844393-396
- ID: 12542
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Abstract
The properties of a statistic called a self-consistent stationarity level of nonstationary time series are examined in this work. It is shown that a change in this statistic can be treated as a disorder in the nonstationarity properties of the series. The significance level of decision making is estimated, characteristic periods in a non-stationary stochastic process are detected, and an optimal sample size for constructing indicators in stochastic control problems is determined. A disorder indicator for electroencephalogram data from epilepsy patients is studied as a practical application.
About the authors
А. A. Kislitsyn
Institute for Applied Mathematics of the Russian Academy of Sciences
Author for correspondence.
Email: alexey.kislitsyn@gmail.com
Russian Federation, 4, Miusskaya square, Moscow, 125047
A. B. Kozlova
N.N. Burdenko National Scientific and Practical Center for Neurosurgery of the Ministry of Healthcare of the Russian Federation
Email: alexey.kislitsyn@gmail.com
Russian Federation, 16, 4-ya Tverskaya-Yamskaya street, Moscow, 125047
M. B. Korsakova
N.N. Burdenko National Scientific and Practical Center for Neurosurgery of the Ministry of Healthcare of the Russian Federation
Email: alexey.kislitsyn@gmail.com
Russian Federation, 16, 4-ya Tverskaya-Yamskaya street, Moscow, 125047
Yu. N. Orlov
Institute for Applied Mathematics of the Russian Academy of Sciences
Email: alexey.kislitsyn@gmail.com
Russian Federation, 4, Miusskaya square, Moscow, 125047
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