Incorporating sparse labels into hidden Markov models using weighted likelihoods improves accuracy and interpretability in biologging studies Incorporating sparse labels into hidden Markov models using weighted likelihoods

Abstract

'Andrew W. Trites' 'Sarah M.E. Fortune' 'Marie Auger-Méthé' 'Vitor Hugo Rodrigues Paiva'] Ecologists often use a hidden Markov model to decode a latent process, such as a sequence of an animal’s behaviours, from an observed biologging time series. Modern technological devices such as video recorders and drones now allow researchers to directly observe an animal’s behaviour. Using these observations as labels of the latent process can improve a hidden Markov model’s accuracy when decoding the latent process. However, many wild animals are observed infrequently. Including such rare labels often has a negligible influence on parameter estimates, which in turn does not meaningfully improve the accuracy of the decoded latent process. We introduce a weighted likelihood approach that increases the relative influence of labelled observations. We use this approach to develop hidden Markov models to decode the foraging behaviour of killer whales (Orcinus orca) off the coast of British Columbia, Canada. Using cross-validated evaluation metrics and a detailed simulation study, we show that our weighted likelihood approach produces more accurate and understandable decoded latent processes compared to existing hidden Markov models and single-frame machine learning methods. Thus, our method effectively leverages sparse labels to enhance researchers’ ability to accurately decode hidden processes across various fields.