Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
An introduction to symbolic dynamics and coding
An introduction to symbolic dynamics and coding
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Novelty detection: a review—part 1: statistical approaches
Signal Processing
Symbolic dynamic analysis of complex systems for anomaly detection
Signal Processing
Symbolic time series analysis via wavelet-based partitioning
Signal Processing - Special section: Distributed source coding
IEEE Transactions on Knowledge and Data Engineering
Pattern identification in dynamical systems via symbolic time series analysis
Pattern Recognition
Anomaly detection in complex system based on epsilon machine
International Journal of Systems Science
Discrete wavelet transform-based time series analysis and mining
ACM Computing Surveys (CSUR)
Improving the classification accuracy of streaming data using SAX similarity features
Pattern Recognition Letters
Hi-index | 0.08 |
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.