A Multivariate Hidden Markov Model for the Identification of Sea Regimes from Incomplete Skewed and Circular Time Series

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moveHMM: an R package for the statistical modelling of animal movement data using hidden Markov models (2016, methodology): “…can also be used, for example to analyse vessel monitoring data to characterize the spatial distribution of ﬁshing eﬀort (Peel, Good & Quinn 2011; Charles, Gillis & Wade 2014), to model wind speed and direction (Holzmann et al. 2006) and to model wave heights and directions (Bulla et al. 2012).”
A multivariate mixed hidden Markov model for blue whale behaviour and responses to sound exposure (2017, methodology): “A possible alternative to this brute-force, but effective, method for finding the global maximum could be to run a few dozen iterations of the expectationmaximisation (EM) algorithm (as it is known to be slightly less sensitive to the choice of initial values) before switching to (much faster) direc”
Real-time FDM machine condition monitoring and diagnosis based on acoustic emission and hidden semi-Markov model (2017, methodology): “The successful applications of HSMM for state identification and fault diagnosis include tool wear monitoring [13,14], early fault detection of gearbox [27], hydraulic pump health monitoring [11], fault diagnosis in chemical multiphase batch process [7], identification of sea regimes [6], and others”