1 research outputs found
Autoregressive Asymmetric Linear Gaussian Hidden Markov Models
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.Comment: 34 pages, 16 figures, intended to be published in IEEE Transactions
on Pattern Analysis and Machine Intelligenc