Modeling Event-driven Time Series with Generalized Hidden Semi-Markov Models

Felix Salfner
This report introduces a new model for event-driven temporal sequence processing: Generalized Hidden Semi-Markov Models (GHSMMs). GHSMMs are an extension of hidden Markov models to continuous time that builds on turning the stochastic process of hidden state traversals into a semi-Markov process. A large variety of probability distributions can be used to specify transition durations. It is shown how GHSMMs can be used to address the principle problems of temporal sequence processing: sequence generation, sequence...
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