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Friday, October 19
Fri, Oct 19, 5:15 PM - 6:30 PM
Hall of Mirrors
Celebrating Women in Statistics and Data Science Reception and Speed Poster 3, Sponsored by Google and 84.51°

Machine Learning Methods for Animal Movement (304965)

*Dhanushi A. Wijeyakulasuriya, Penn State University 
Ephraim M. Hanks, Penn State University 
Benjamin A Shaby, Penn State University 

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 an ensemble 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.