TY - JOUR
T1 - Symbolic time series analysis for anomaly detection
T2 - A comparative evaluation
AU - Chin, Shin C.
AU - Ray, Asok
AU - Rajagopalan, Venkatesh
N1 - Funding Information:
This work has been supported in part by Army Research Office (ARO) under Grant No. DAAD19-01-1-0646; and NASA Glenn Research Center under Grant No. NNC04GA49G.
Copyright:
Copyright 2008 Elsevier B.V., All rights reserved.
PY - 2005/9
Y1 - 2005/9
N2 - 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.
AB - 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.
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U2 - 10.1016/j.sigpro.2005.03.014
DO - 10.1016/j.sigpro.2005.03.014
M3 - Article
AN - SCOPUS:21744450636
SN - 0165-1684
VL - 85
SP - 1859
EP - 1868
JO - Signal Processing
JF - Signal Processing
IS - 9
ER -