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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...
In this paper we present a method for identification of temporal patterns that are predictive of events in a dynamic data system. The proposed new MRPS-HMM method applies a hybrid model using Reconstructed Phase Space (RPS) and stochastic state estimation via Hidden Markov Model (HMM) to search predictive patterns. This method constructs a multivariate phase space by embedding each data sequence with...
In this paper we present a method for detecting multivariate temporal patterns that are characteristic and predictive of significant events in a multivariate dynamic data system. A new hybrid RPS-GMM method is applied to identify patterns. This method constructs phase space embedding by using individual embedding of each variable sequences. We employ discriminative approach by applying Gaussian Mixture...
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