Autoregressive Asymmetric Linear Gaussian Hidden Markov Models

• URL: http://arxiv.org/abs/2010.15604v1
• Date: Tue, 27 Oct 2020 08:58:46 GMT
• Title: Autoregressive Asymmetric Linear Gaussian Hidden Markov Models
• Authors: Carlos Puerto-Santana and Pedro Larra\~naga and Concha Bielza
• Abstract summary: Asymmetric hidden Markov models provide a framework where the trend of the process can be expressed as a latent variable. We show how inference, hidden states decoding and parameter learning must be adapted to fit the proposed model.
• Score: 1.332091725929965
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In a real life process evolving over time, the relationship between its relevant variables may change. Therefore, it is advantageous to have different inference models for each state of the process. Asymmetric hidden Markov models fulfil this dynamical requirement and provide a framework where the trend of the process can be expressed as a latent variable. In this paper, we modify these recent asymmetric hidden Markov models to have an asymmetric autoregressive component, allowing the model to choose the order of autoregression that maximizes its penalized likelihood for a given training set. Additionally, we show how inference, hidden states decoding and parameter learning must be adapted to fit the proposed model. Finally, we run experiments with synthetic and real data to show the capabilities of this new model.

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