Thirteenth International Symposium on Temporal Representation and Reasoning (TIME'06) Adaptive Interpolation Algorithms for Temporal-Oriented Datasets Budapest, Hungary June 15-June 17 ISBN: 0-7695-2617-9
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/TIME.2006.4
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. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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