Non-homogeneous hidden Markov-switching models for wind time series
Abstract
In this paper we propose various Markov-switching auotoregressive models for bivariate time series which describe wind conditions at a single location. The main originality of the proposed models is that the hidden Markov chain is not homogeneous, its evolution depending on the past wind conditions. It is shown that they permit to reproduce complex features of wind time series such as non-linear dynamics and the multimodal marginal distributions.
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