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Event Characterization and Prediction Based on Temporal Patterns in Dynamic Data System
Jan. 2014 (vol. 26 no. 1)
pp. 144-156
Wenjing Zhang, Marquette University, Milwaukee
Xin Feng, Marquette University, Milwaukee
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.
Index Terms:
Materials requirements planning,Data systems,Optimization,Linear programming,Delay effects,Vectors,Euclidean distance,dynamic data system,Temporal pattern,reconstructed phase space,Gaussian mixture models,optimization
Citation:
Wenjing Zhang, Xin Feng, "Event Characterization and Prediction Based on Temporal Patterns in Dynamic Data System," IEEE Transactions on Knowledge and Data Engineering, vol. 26, no. 1, pp. 144-156, Jan. 2014, doi:10.1109/TKDE.2013.60
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