A Comparison of Neighbourhood Selection Techniques in Spatio-Temporal Forecasting Models
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 loc...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Copernicus Publications
2014-11-01
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| Series: | The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| Online Access: | http://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XL-2/7/2014/isprsarchives-XL-2-7-2014.pdf |
| Summary: | 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 L<sub>1</sub> penalisation are effective. In particular, the maximum concave penalty (MCP) method selects parsimonious
models that produce good forecasting performance. |
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| ISSN: | 1682-1750 2194-9034 |