Spatiotemporal Relational Random Forests

Authors:
Timothy A. Supinie;Amy McGovern;John Williams;Jennifer Abernathy
Affiliations:
-;-;-;-
Venue:
ICDMW '09 Proceedings of the 2009 IEEE International Conference on Data Mining Workshops
Year:
2009

Citing 0
Cited 3

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Abstract

We introduce and validate Spatiotemporal Relational Random Forests, which are random forests created with spatiotemporal relational probability trees. We build on the documented success of random forests by bringing spatiotemporal capabilities to the trees, enabling them to identify critical spatial, temporal, and spatiotemporal features in the data. We validate our results on simulated data and real-world convectively-induced turbulence data from a commercial airline flying in the continental United States.