Anomaly recognition in recordings using fuzzy logic
https://doi.org/10.22405/2226-8383-2025-26-3-6-43
Abstract
One of the areas of time series analysis is the study of their morphology. The results of such a study are used to detect various types of anomalies in the behavior of the series, moments of
restructuring its behavior, etc.
This paper presents a program for exploring records using DMA methods – a new researcheroriented data approach that makes extensive use of fuzzy mathematics. Its input data is a record
expressing a process with discrete time and some property of the process whose fulfillment is of interest to the researcher.
The manifestation of a property on a record is formalized as a fuzzy structure (measure of manifestation) on the do-main of the record definition, which expresses the degree of manifestation of the property in question. The measure of manifestation of a property is the
basis for dividing the record into regular, transient, and abnormal manifestations of the property in question on a record. This division gives the researcher a simple and meaningful idea of the property manifestation on a record that interests him.
The purpose of this paper is to improve on the current DMA decomposition of such a decomposition.
About the Authors
Sergey Martikovich AgayanRussian Federation
doctor of physical and mathematical sciences
Shamil Rafekovich Bogoutdinov
Russian Federation
candidate of physical and mathematical sciences
Dmitry Alfredovich Kamaev
Russian Federation
doctor of technical sciences
Boris Arkadevich Dzeboev
Russian Federation
doctor of physical and mathematical sciences
Michael Nikolaevich Dobrovolsky
Russian Federation
candidate of physical and mathematical sciences
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Review
For citations:
Agayan S.M., Bogoutdinov Sh.R., Kamaev D.A., Dzeboev B.A., Dobrovolsky M.N. Anomaly recognition in recordings using fuzzy logic. Chebyshevskii Sbornik. 2025;26(3):6-43. https://doi.org/10.22405/2226-8383-2025-26-3-6-43
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