Adaptive kernel smoothing regression for spatio-temporal environmental datasets

Abstract

A method for performing kernel smoothing regression in an incremental, adaptive manner is described. A simple and fast combination of incremental vector quantization with kernel smoothing regression using adaptive bandwidth is shown to be effective for online modeling of environmental datasets. The approach proposed is to apply kernel smoothing regression in an incremental estimation of the (evolving) probability distribution of the incoming data stream rather than the whole sequence of observations. The method is illustrated on publicly available datasets corresponding to the Tropical Atmosphere Ocean array and the Helsinki Commission hydrographic database for the Baltic Sea.