Gaussian Process for Trajectories
This is an incremental application of existing Gaussian process methods to geospatial trajectory data.
The chapter describes Gaussian processes as an interpolation technique for geospatial trajectories, modeling measurements as a multidimensional Gaussian to produce predictions with uncertainty for each timestamp.
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.