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DLOREAN: Dynamic Location-aware Reconstruction of multiway Networks

Fredrik Johansson (Institutionen för data- och informationsteknik, Datavetenskap (Chalmers)) ; Vinay Jethava (Institutionen för data- och informationsteknik, Datavetenskap (Chalmers)) ; Devdatt Dubhashi (Institutionen för data- och informationsteknik, Datavetenskap (Chalmers))
2013 13th IEEE International Conference on Data Mining Workshops, ICDMW 2013; Dallas, TX; United States; 7 December 2013 through 10 December 2013 p. 1012-1019. (2013)
[Konferensbidrag, refereegranskat]

This paper presents a method for learning time-varying higher-order interactions based on node observations, with application to short-term traffic forecasting based on traffic flow sensor measurements. We incorporate domain knowledge into the design of a new damped periodic kernel which lever- ages traffic flow patterns towards better structure learning. We introduce location-based regularization for learning models with desirable geographical properties (short-range or long-range interactions). We show using experiments on synthetic and real data, that our approach performs better than static methods for reconstruction of multiway interactions, as well as time-varying methods which recover only pair-wise interactions. Further, we show on real traffic data that our model is useful for short-term traffic forecasting, improving over state-of-the-art.

Nyckelord: Traffic prediction; structure learning; higher-order; spatio-temporal; kernel-reweighting; hierarchical inclusion

Article number 6754033

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Denna post skapades 2014-01-07. Senast ändrad 2016-07-07.
CPL Pubid: 191620


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