DISCRIMINANT APPROACH TO DETECT ANOMALIES USING MARKOV SEQUENCES
Alexander V. Skatkov, A A Bryukhovetskiy, Dmitriy V. Moiseev, Iu.E. Shishkin · Monitoring systems of environment · 2019
The work describes a discriminant approach and a program system for detecting anomalies of processes occurring in the ecosystem of the water area basing on the Markov model.The conditions and results of the statistical simulation experiment are presented in order to compare the reliability of the analytical and simulation models and determine the control and warning boundaries for making decisions on the presence of anomalies.To solve the problem of detecting process anomalies according to the monitoring of the water area, an adaptive method based on a discrete wavelet decomposition of observation data and a statistical detection algorithm is proposed.To adapt the wavelet transform to the task of identifying process anomalies according to monitoring data, it is proposed to use the sliding window method.