Dynamic bayesian networks: representation, inference and learning
Dynamic bayesian networks: representation, inference and learning
Temporal causal modeling with graphical granger methods
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Spatial-temporal causal modeling for climate change attribution
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Multivariate short-term traffic flow forecasting using time-series analysis
IEEE Transactions on Intelligent Transportation Systems
Measuring autonomy and emergence via granger causality
Artificial Life
Granger causality analysis on IP traffic and circuit-level energy monitoring
Proceedings of the 2nd ACM Workshop on Embedded Sensing Systems for Energy-Efficiency in Building
A bayesian network approach to traffic flow forecasting
IEEE Transactions on Intelligent Transportation Systems
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In this paper we propose a binary Bayesian network to model the speed variations for traffic speed prediction. Comparing to continuous graphical models, firstly, our method reduces the complexity of the model. Secondly, we use Granger causality test to determine the structure and parameters of the Bayesian network. Experiments on large GPS data of vans in the freeway network illustrate the good performance of our model.