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EVENT CHARACTERIZATION AND PREDICTION BASED ON TEMPORAL
PATTERNS IN DYNAMIC DATA SYSTEM
ABSTRACT:
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.