Prediction and Inference in a Partially Hidden Markov-switching Framework with Autoregression. Application to Machinery Health Diagnosis

2021 IEEE 33rd International Conference on Tools with Artificial Intelligence (ICTAI)（2021）

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摘要

Time series subject to changes in regime are encountered in multiple applications. Models such as the renowned Hidden Markov Model (HMM) describe time series whose states are unknown at all time-steps. In some situations, partial knowledge on states is available. In this paper, we describe the Partially Hidden Markov Chain Linear AutoRegressive (PHMC-LAR) model. This model combines a HMM framework...

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关键词

Times series,Markov regime-switching models,autoregressive models,partial state annotation,semi-supervised learning,Expectation-Maximization,machinery health diagnosis,system security

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