Neuroevolution Methods for Organizing the Search for Anomalies in Time Series
Serhii Leoshchenko, Аndrii Oliinyk, С. А. Субботин, Matviy Ilyashenko, Тетяна Олексіївна Колпакова · 2023
This paper is devoted to the problem of detecting and classifying anomalies for time series data.Some of the important applications of time series anomaly detection are healthcare, fraud detection, and system failure recognition.Despite scientists extensive experience in detecting anomalies in time series, most methods look for individual objects that differ from ordinary objects, but do not take into account the specifics of the data sequence [1].In this paper, a method for detecting anomalies and clastering time series based on neuroevolutionary approaches is proposed.Prediction-based methods are used to detect anomalies: statistical and deep neural networks are used.Classical clustering methods that accept statistical parameters of series were used for clustering.