The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Publications Copernicus
Articles | Volume XL-2
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XL-2, 7–12, 2014
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XL-2, 7–12, 2014

  11 Nov 2014

11 Nov 2014

A Comparison of Neighbourhood Selection Techniques in Spatio-Temporal Forecasting Models

J. Haworth and T. Cheng J. Haworth and T. Cheng
  • SpaceTimeLab, Dept. of Civil, Environmental and Geomatic Engineering, University College London, Gower Street, London, WC1E 6BT, UK

Keywords: Spatio-temporal, forecasting, prediction, transportation, model selection, variable selection

Abstract. Spatio-temporal neighbourhood (STN) selection is an important part of the model building procedure in spatio-temporal forecasting. The STN can be defined as the set of observations at neighbouring locations and times that are relevant for forecasting the future values of a series at a particular location at a particular time. Correct specification of the STN can enable forecasting models to capture spatio-temporal dependence, greatly improving predictive performance. In recent years, deficiencies have been revealed in models with globally fixed STN structures, which arise from the problems of heterogeneity, nonstationarity and nonlinearity in spatio-temporal processes. Using the example of a large dataset of travel times collected on London’s road network, this study examines the effect of various STN selection methods drawn from the variable selection literature, varying from simple forward/backward subset selection to simultaneous shrinkage and selection operators. The results indicate that STN selection methods based on L1 penalisation are effective. In particular, the maximum concave penalty (MCP) method selects parsimonious models that produce good forecasting performance.