loading...
Adaptive Interpolation Algorithms for Temporal-Oriented Datasets
Budapest, Hungary June 15-June 17
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/TIME.2006.4Thirteenth International Symposium on ...
 This Article 
 
PDF
HTML
 
 Share 
   
 Bibliographic References 
   
 Add to: 
 
Digg
Furl
Spurl
Blink
Simpy
Google
Del.icio.us
Y!MyWeb
 
 Search 
   
Jun Gao, University of Nebraska-Lincoln
Spatiotemporal datasets can be classified into two categories: temporal-oriented and spatial-oriented datasets depending on whether missing spatiotemporal values are closer to the values of its temporal or spatial neighbors. We present an adaptive spatiotemporal interpolation model that can estimate the missing values in both categories of spatiotemporal datasets. The key parameters of the adaptive spatiotemporal interpolation model can be adjusted based on experience.
Citation:
Jun Gao, "Adaptive Interpolation Algorithms for Temporal-Oriented Datasets," time, pp.145-151, Thirteenth International Symposium on Temporal Representation and Reasoning (TIME'06), 2006
Usage of this product signifies your acceptance of the Terms of Use.