Symbolic time series analysis for anomaly detection: A comparative evaluation

Recent literature has reported a novel method for anomaly detection in complex dynamical systems, which relies on symbolic time series analysis and is built upon the principles of automata theory and pattern recognition. This paper compares the performance of this symbolic-dynamics-based method with that of other existing pattern recognition techniques from the perspectives of early detection of small anomalies. Time series data of observed process variables on the fast time-scale of dynamical systems are analyzed at slow time-scale epochs of (possible) anomalies. The results are derived from experiments on a nonlinear electronic system with a slowly varying dissipation parameter.