Interpreting the definition of time series anomalies using fuzzy ontologies

Abstract

The paper describes an approach to detecting time series anomalies, taking into account the specifics of the subject area, represented as a fuzzy ontology. The approach involves the use of LSTM (long short-term memory) networks for the mathematical search for anomalies, fuzzy ontology allows you to filter the detection results and draw an inference for decision making.