Gaussian Processes for time-marked time-series data

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John Cunningham, Zoubin Ghahramani, Carl Rasmussen ;
Proceedings of the Fifteenth International Conference on Artificial Intelligence and Statistics, PMLR 22:255-263, 2012.

Abstract

In many settings, data is collected as multiple time series, where each recorded time series is an observation of some underlying dynamical process of interest. These observations are often time-marked with known event times, and one desires to do a range of standard analyses. When there is only one time marker, one simply aligns the observations temporally on that marker. When multiple time-markers are present and are at different times on different time series observations, these analyses are more difficult. We describe a Gaussian Process model for analyzing multiple time series with multiple time markings, and we test it on a variety of data.

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