Systems monitoring based on robust estimation of stochastic time series models

Abstract

The problem of system monitoring under conditions of stochastic data heterogeneity based on time series models is considered. The stability of monitoring is proposed to be ensured through the use of convex-concave loss functions. An algorithm for estimating the variance of the main error distribution is proposed. This allows using robust procedures for estimating the parameters of stochastic time series models without a priori information about the variance value of the main error distribution. Using the Monte Carlo statistical test method, the estimates of the proposed robust methods are compared with the known methods of least squares, least modules, and Huber. It is shown that the introduced robust estimates of the parameters of stochastic models of time series win in accuracy and allow increasing the reliability of monitoring the state of systems.