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Event Characterization and Prediction Based on Temporal Patterns in Dynamic Data System

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2 Author(s)
Wenjing Zhang ; Dept. of Electr. & Comput. Eng., Marquette Univ., Milwaukee, WI, USA ; Xin Feng

The new method proposed in this paper applies a multivariate reconstructed phase space (MRPS) for identifying multivariate temporal patterns that are characteristic and predictive of anomalies or events in a dynamic data system. The new method extends the original univariate reconstructed phase space framework, which is based on fuzzy unsupervised clustering method, by incorporating a new mechanism of data categorization based on the definition of events. In addition to modeling temporal dynamics in a multivariate phase space, a Bayesian approach is applied to model the first-order Markov behavior in the multidimensional data sequences. The method utilizes an exponential loss objective function to optimize a hybrid classifier which consists of a radial basis kernel function and a log-odds ratio component. We performed experimental evaluation on three data sets to demonstrate the feasibility and effectiveness of the proposed approach.

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Knowledge and Data Engineering, IEEE Transactions on  (Volume:26 ,  Issue: 1 )