Clustering time-series data into discrete groups can improve prediction as well as providing
insight into the nature of underlying, unobservable states of the system. However,
temporal heterogeneity and autocorrelation (persistence) in group occupancy
can obscure such signals. We use latent-state and hidden Markov models (HMMs),
two standard clustering techniques, to model high-resolution hourly movement data
from Florida panthers. Allowing for temporal heterogeneity in transition probabilities,
a straightforward but rarely explored model extension, resolves previous HMM
modeling issues and clarifies the behavioural patterns of panthers. / Thesis / Master of Science (MSc)
| Identifer | oai:union.ndltd.org:mcmaster.ca/oai:macsphere.mcmaster.ca:11375/18321 |
| Date | 11 1900 |
| Creators | Li, Michael |
| Contributors | Bolker, Benjamin, Statistics |
| Source Sets | McMaster University |
| Language | English |
| Detected Language | English |
| Type | Thesis |
Page generated in 0.0017 seconds