Hidden Markov models for circular and linear-circular time series

By Hajo Holzmann, Axel Munk, Milan Šuster, Walter Zucchini. Environmental and Ecological Statistics, 2006

109 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Genetics, Signal Processing

View Paper: https://www.semanticscholar.org/paper/b50bb18780cc6e80a0ecd00830900b43ba373f74

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Citations in the corpus

State-space models of individual animal movement. (2008, background): “Switches between modes could also be modelled as functions of time (to capture the effect of age or season), internal state (energetics, life history status) [ 57 ] or environmental characteristics (navigational cues, resources, habitat selection) [42].”
moveHMM: an R package for the statistical modelling of animal movement data using hidden Markov models (2016, methodology): “In movement ecology, HMMs have successfully been used to analyse the movement of, inter alia , caribou (Franke, Caelli & Hudson 2004), fruit ﬂies (Holzmann et al. 2006), tuna (Patterson et al. 2009), panthers (van de Kerk et al. 2015), woodpeckers (McKellar et al. 2015) and white sharks (Towner et…”
A Multivariate Hidden Markov Model for the Identification of Sea Regimes from Incomplete Skewed and Circular Time Series (2012, methodology): “Our classification procedure is motivated by issues that arise in marine studies, but can be easily adapted to a wide range of real-world cases, including for example ecological studies of animal behavior, where direction and speed of movements are recorded (Holzmann et al. 2006), and bioinformatics”
Flexible and practical modeling of animal telemetry data: hidden Markov models and extensions. (2012, methodology): “All of these distributions have been used previously in animal movement models (Morales et al. 2004, Holzmann et al. 2006) and are equally easy to fit in the HMM framework.”
Hidden Markov Model on a unit hypersphere space for gesture trajectory recognition (2014, methodology): “Although Holzmann et al. (2006) considered time-series modeling using an HMM, it was for modeling sequence of unit-sphere data, i.e. angle.”
Non-homogeneous hidden Markov-switching models for wind time series (2015, background): “Several autoregressive models have been proposed in the literature for modeling directional time series (see [5], [11],[15], [17]).”