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

Automated summary

This paper proposes an approach to detecting time series anomalies by integrating LSTM neural network and Fuzzy OWL ontology. The method involves mathematical anomaly search using LSTM network and inference using fuzzy ontology. The proposed software system architecture is presented, and computational experiments on drilling rigs data are conducted. The high efficiency of the approach is demonstrated, but further improvements are planned in terms of neural network architecture and automatic inference rule generation.