CONTINUOUS-TIME CORRELATED RANDOM WALK MODEL FOR ANIMAL TELEMETRY DATA
We propose a continuous-time version of the correlated random walk model for animal telemetry data. The continuous-time formulation allows data that have been nonuniformly collected over time to be modeled without subsampling, interpolation, or aggregation to obtain a set of locations uniformly spac...
| Main Authors: | , , , |
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| Format: | Article in Journal/Newspaper |
| Language: | unknown |
| Published: |
Figshare
2016
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| Subjects: | |
| Online Access: | https://dx.doi.org/10.6084/m9.figshare.c.3300326.v1 https://figshare.com/collections/CONTINUOUS-TIME_CORRELATED_RANDOM_WALK_MODEL_FOR_ANIMAL_TELEMETRY_DATA/3300326/1 |
| Summary: | We propose a continuous-time version of the correlated random walk model for animal telemetry data. The continuous-time formulation allows data that have been nonuniformly collected over time to be modeled without subsampling, interpolation, or aggregation to obtain a set of locations uniformly spaced in time. The model is derived from a continuous-time Ornstein-Uhlenbeck velocity process that is integrated to form a location process. The continuous-time model was placed into a state–space framework to allow parameter estimation and location predictions from observed animal locations. Two previously unpublished marine mammal telemetry data sets were analyzed to illustrate use of the model, by-products available from the analysis, and different modifications which are possible. A harbor seal data set was analyzed with a model that incorporates the proportion of each hour spent on land. Also, a northern fur seal pup data set was analyzed with a random drift component to account for directed travel and ocean currents. |
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