Keywords: deep learning, animal movement, time series
Animal movement drives important ecological processes such as migration and the spread of infectious disease. Current approaches to modeling animal tracking data focus on parametric models used to understand environmental effects on movement behavior and to fill in missing tracking data. Deep learning algorithms are powerful and flexible predictive modeling tools, but have never been applied to animal movement data. In this study we present a neural networks approach to modeling and predicting animal movement. Our approach considers predicting an animal's displacement at each time step using the position and velocity of the animal at several previous time lags. We apply this approach to ant movement data.