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Pattern Identification Using Reconstructed Phase Space and Hidden Markov Model

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

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 estimated time-delay and dimension. Multivariate sequences are categorized into three states: normal, patterns and events which are estimated by HMM. A penalized exponential loss function is used to estimates the optimal weights of each module of the classifier. We performed two experimental applications including a chaotic Rossler Map series and sludge volume bulking forecasting problem. Experiments results show that the new method significantly outperforms baseline methods.

Published in:

Machine Learning and Applications (ICMLA), 2012 11th International Conference on  (Volume:1 )

Date of Conference:

12-15 Dec. 2012