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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
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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