Gaussian Process for Trajectories

TL;DR

This paper discusses using Gaussian processes as a flexible method for interpolating and modeling geospatial trajectories, capturing complex trends and uncertainty in spatiotemporal data.

Contribution

It provides a detailed explanation of applying Gaussian processes to trajectories, including implementation considerations and example applications.

Findings

Gaussian processes effectively model trajectory data with uncertainty.

The method captures complex spatial and temporal trends.

Implementation details facilitate practical application.

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.