NDLI: DLOREAN: Dynamic Location-Aware Reconstruction of Multiway Networks

Content Provider	IEEE Xplore Digital Library
Author	Johansson, F. Jethava, V. Dubhashi, D.
Copyright Year	2013
Description	Author affiliation: Chalmers Univ., Goteborg, Sweden (Johansson, F.; Jethava, V.; Dubhashi, D.)
Abstract	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 leverages 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.
Sponsorship	Toshiba
Starting Page	1012
Ending Page	1019
File Size	322979
Page Count	8
File Format	PDF
e-ISBN	9781479931422
DOI	10.1109/ICDMW.2013.57
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-12-07
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Traffic prediction Computational modeling Forecasting Optimization hierarchical inclusion structure learning Graphical models kernel-reweighting Market research Data models higher-order Kernel spatio-temporal
Content Type	Text
Resource Type	Article

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