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Hierarchical hidden Markov models for multi-scale time series

While conventional hidden Markov models (HMMs) are restricted to modeling single-scale data, in practice variables are often observed at different temporal resolutions. An economy’s gross domestic product, e.g., is typically observed on a yearly, quarterly, or monthly basis, whereas stock prices are available daily or at even finer resolutions. Hierarchical HMMs regard the observations as stemming from multiple, connected state processes, each of which operates at the time scale at which the corresponding variables were observed.



Click here to find out more about hierarchical HMMs.

Markov-switching generalised additive models for location, scale, and shape

Markov-switching generalized additive models for location, scale, and shape (Markov-switching GAMLSS) constitute a novel class of flexible latent-state time series regression models. In contrast to conventional Markov-switching regression models, they can be used to model different state-dependent parameters of the response distribution - not only the mean, but also e.g. the variance - as potentially smooth functions of explanatory variables.



Click here to find out more about Markov-switching GAMLSS.

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Teaching experience 1

Undergraduate course, University 1, Department, 2014

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Teaching experience 2

Workshop, University 1, Department, 2015

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