Related papers: Gaussian Process for Trajectories

Gaussian Process for Trajectories

URL: http://arxiv.org/abs/2110.03712v1
Date: Thu, 7 Oct 2021 18:02:19 GMT
Title: Gaussian Process for Trajectories
Authors: Kien Nguyen, John Krumm, Cyrus Shahabi
Abstract summary: We discuss elements that need to be considered when applying Gaussian process to timestamps, common choices for those elements, and provide a concrete example of implementing a Gaussian process.
Score: 17.458493494904992
License: http://creativecommons.org/licenses/by/4.0/
Abstract: The Gaussian process is a powerful and flexible technique for interpolating spatiotemporal data, especially with its ability to capture complex trends and uncertainty from the input signal. This chapter describes Gaussian processes as an interpolation technique for geospatial trajectories. A Gaussian process models measurements of a trajectory as coming from a multidimensional Gaussian, and it produces for each timestamp a Gaussian distribution as a prediction. We discuss elements that need to be considered when applying Gaussian process to trajectories, common choices for those elements, and provide a concrete example of implementing a Gaussian process.

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