Topology-regularized universal vector autoregression for traffic forecasting in large urban areas
作者：
Highlights：
• Fast paced growth in urban areas will soon drive traffic forecasting systems obsolete.
• Next generation systems should be: topological, modular, scalable, online & nonlinear.
• The proposed method with such properties has low network wide generalization error.
• Method outperforms baselines and univariate equivalents over two large area datasets.
• The topological design adjacency matrix is pivotal & requires expert domain knowledge.
摘要
•Fast paced growth in urban areas will soon drive traffic forecasting systems obsolete.•Next generation systems should be: topological, modular, scalable, online & nonlinear.•The proposed method with such properties has low network wide generalization error.•Method outperforms baselines and univariate equivalents over two large area datasets.•The topological design adjacency matrix is pivotal & requires expert domain knowledge.
论文关键词：Topology regularized universal vector autoregression,Multivariate timeseries forecasting,Spatiotemporal autocorrelation,Traffic prediction,Big data,Structural risk minimization
论文评审过程：Received 12 October 2016, Revised 15 March 2017, Accepted 5 April 2017, Available online 7 April 2017, Version of Record 19 April 2017.
论文官网地址：https://doi.org/10.1016/j.eswa.2017.04.015
