High dimensional regression for regenerative time-series: an application to road traffic modeling
This work addresses road traffic modeling for urban planning, but it is incremental as it applies an existing L1-penalized method to a specific high-dimensional time-series context.
The authors tackled the problem of predicting high-dimensional daily road traffic time-series using a vector autoregressive model with L1 penalization, achieving competitive prediction performance compared to neural networks and enabling identification of key road sections.
A statistical predictive model in which a high-dimensional time-series regenerates at the end of each day is used to model road traffic. Due to the regeneration, prediction is based on a daily modeling using a vector autoregressive model that combines linearly the past observations of the day. Due to the high-dimension, the learning algorithm follows from an L1-penalization of the regression coefficients. Excess risk bounds are established under the high-dimensional framework in which the number of road sections goes to infinity with the number of observed days. Considering floating car data observed in an urban area, the approach is compared to state-of-the-art methods including neural networks. In addition of being highly competitive in terms of prediction, it enables the identification of the most determinant sections of the road network.