Gaussian Process for Trajectories

Kien Nguyen; John Krumm; Cyrus Shahabi

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arXiv · 2110.03712

Gaussian Process for Trajectories

Kien Nguyen·John Krumm·Cyrus Shahabi

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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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BibTeX RIS

Kien Nguyen, John Krumm, Cyrus Shahabi. 2021-10-07. Gaussian Process for Trajectories. https://arxiv.org/abs/2110.03712

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