Integration of Fuzzy Ontologies and Neural Networks in the Detection of Time Series Anomalies

This paper explores an approach to solving the problem of detecting time series anomalies, taking into account specifics subject area. We propose a method based on integration neural network with long short-term memory (LSTM) and Fuzzy OWL (Fuzzy Web Ontology Language) ontology. A LSTM is used for mathematical search anomalies in first stage. The fuzzy ontology filters detection results draws inference decision making second contains formalized representation objects area rules that select only those anomaly values correspond this In article, we architecture software system implements approach. Computational experiments were carried out free data technical characteristics drilling rigs. showed high efficiency, but not maximum efficiency proposed future, plan more efficient detection. also develop algorithm automatically filling when analyzing text sources.