A friendly guide to wavelets
The lifting scheme: a construction of second generation wavelets
SIAM Journal on Mathematical Analysis
Pattern Discovery by Residual Analysis and Recursive Partitioning
IEEE Transactions on Knowledge and Data Engineering
A Maximum Variance Cluster Algorithm
IEEE Transactions on Pattern Analysis and Machine Intelligence
Symbolic dynamic analysis of complex systems for anomaly detection
Signal Processing
Nonlinear Time Series Analysis
Nonlinear Time Series Analysis
Clustering of time series data-a survey
Pattern Recognition
Computational Signal Processing with Wavelets
Computational Signal Processing with Wavelets
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
Behavior recognition in mobile robots using symbolic dynamic filtering and language measure
ACC'09 Proceedings of the 2009 conference on American Control Conference
ACC'09 Proceedings of the 2009 conference on American Control Conference
Signal threshold estimation in a sensor field for undersea target tracking
ACC'09 Proceedings of the 2009 conference on American Control Conference
Underwater mine detection using symbolic pattern analysis of sidescan sonar images
ACC'09 Proceedings of the 2009 conference on American Control Conference
Symbolic analysis of time series signals using generalized Hilbert transform
ACC'09 Proceedings of the 2009 conference on American Control Conference
Improving the classification accuracy of streaming data using SAX similarity features
Pattern Recognition Letters
Optimization of symbolic feature extraction for pattern classification
Signal Processing
Adaptive pattern classification for symbolic dynamic systems
Signal Processing
Adaptive fault detection and diagnosis using an evolving fuzzy classifier
Information Sciences: an International Journal
Performance comparison of feature extraction algorithms for target detection and classification
Pattern Recognition Letters
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Symbolic time series analysis (STSA) of complex systems for anomaly detection has been recently introduced in literature. An important feature of the STSA method is extraction of relevant information, imbedded in the measured time series data, to generate symbol sequences. This paper presents a wavelet-based partitioning approach for symbol generation, instead of the currently practiced method of phase-space partitioning. Various aspects of the proposed technique, such as wavelet selection, noise mitigation, and robustness to spurious disturbances, are discussed. The wavelet-based partitioning in STSA is experimentally validated on laboratory apparatuses for anomaly/damage detection. Its efficacy is investigated by comparison with phase-space partitioning.