@article {1969991,
	title = {Wavelet Methods in Interpolation of High-Frequency Spatial-Temporal Pressure},
	journal = {Spatial Statistics},
	volume = {8},
	year = {2014},
	month = {2014},
	pages = {52{\textendash}68},
	abstract = {The location-scale and whitening properties of wavelets make them more favorable for interpolating high-frequency monitoring data than Fourier-based methods. In the past, wavelets have been used to simplify the dependence structure in multiple time or spatial series, but little has been done to apply wavelets as a modeling tool in a space{\textendash}time setting, or, in particular, to take advantage of the localization of wavelets to capture the local dynamic characteristics of high-frequency meteorological data. This paper analyzes minute-by-minute atmospheric pressure data from the Atmospheric Radiation Measurement program using different wavelet coefficient structures at different scales and incorporating spatial structure into the model. This approach of modeling space{\textendash}time processes using wavelets produces accurate point predictions with low uncertainty estimates, and also enables interpolation of available data from sparse monitoring stations to a high density grid and production of meteorological maps on large spatial and temporal scales.},
	keywords = {Business Analytics},
	author = {Chang,Xiaohui and Stein,Michael L.}
}