ReFuzzTiD: A Recurrent Neurofuzzy Model for Anomaly Detection in Time Series
George Kandilogiannakis, Paris Mastorocostas · 2020
In this paper a recurrent neurofuzzy model is proposed, for time series anomaly detection. ReFuzzTiD comprises fuzzy rules whose consequent parts are simple three-layer neural networks with internal feedback at the neurons of the hidden layer. ReFuzzTid is trained by the Simulated Annealing Dynamic Resilient Propagation algorithm. The model learns the dynamics of the time series such that it can classify them by detecting the anomaly points. A comparative analysis with a series of time series anomaly detection models is given, highlighting the characteristics of the proposed detector.