Statistical aspects of model selection
From data to model
A simple description of spatial-temporal processes
Computational Statistics & Data Analysis
Spatio-temporal prediction of snow water equivalent using the Kalman filter
Computational Statistics & Data Analysis
Adaptive filter theory (3rd ed.)
Adaptive filter theory (3rd ed.)
System identification (2nd ed.): theory for the user
System identification (2nd ed.): theory for the user
Stochastic Complexity in Statistical Inquiry Theory
Stochastic Complexity in Statistical Inquiry Theory
Processing of Multidimensional Signals
Processing of Multidimensional Signals
ECCV '96 Proceedings of the 4th European Conference on Computer Vision-Volume II - Volume II
Kriging Interpolation on High-Performance Computers
HPCN Europe 1998 Proceedings of the International Conference and Exhibition on High-Performance Computing and Networking
A Problem Oriented Approach to Data Mining in Distributed Spatio-temporal Database
ICCS '07 Proceedings of the 7th international conference on Computational Science, Part III: ICCS 2007
A spatio-temporal auto regressive model for frame rate upconversion
IEEE Transactions on Circuits and Systems for Video Technology
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A novel method is proposed for forecasting spatial-temporal data with a short observation history sampled on a uniform grid. The method is based on spatial-temporal autoregressive modeling where the predictions of the response at the subsequent temporal layer are obtained using the response values from a recent history in a spatial neighborhood of each sampling point. Several modeling aspects such as covariance structure and sampling, as well as identification, model estimation and forecasting issues, are discussed. Extensive experimental evaluation is performed on synthetic and real-life data. The proposed forecasting models were shown capable of providing a near optimal prediction accuracy on simulated stationary spatial-temporal data in the presence of additive noise and a correlated model error. Results on a spatial-temporal agricultural dataset indicate that the proposed methods can provide useful prediction on complex real-life data with a short observation history.