The calculi of emergence: computation, dynamics and induction
Proceedings of the NATO advanced research workshop and EGS topical workshop on Chaotic advection, tracer dynamics and turbulent dispersion
A symbolic representation of time series, with implications for streaming algorithms
DMKD '03 Proceedings of the 8th ACM SIGMOD workshop on Research issues in data mining and knowledge discovery
Symbolic dynamic analysis of complex systems for anomaly detection
Signal Processing
Assumption-free anomaly detection in time series
SSDBM'2005 Proceedings of the 17th international conference on Scientific and statistical database management
Symbolic time series analysis via wavelet-based partitioning
Signal Processing - Special section: Distributed source coding
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Epsilon machine is a computational mechanics theory and its most effective reconstruction algorithm is causal state splitting reconstruction (CSSR). As CSSR can only be applied to symbol series, symbolising real series to symbol series is necessary in practice. Epsilon machine discovers the hidden pattern of a system. In reconstructed results, the hidden pattern is expressed as the set of causal states. Based on the variation of causal states, a novel anomaly detection algorithm, structure vector model, is presented. The vector is composed of the causal states, and the anomaly measure is defined with the distance of different vectors. An example of the crankshaft fatigue demonstrates the effectiveness of the model. The mechanism of the model is discussed in detail from three aspects, computational mechanics, symbolic dynamics and complex networks. The new idea defining anomaly measure based on the variation of hidden patterns can be interpreted reasonably with the hierarchical structure of complex networks. The jump in anomaly curves is a nature candidate for the threshold, which confirms the positive meaning of the model. Finally, the parameter choice and time complexity are briefly analysed.