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We address the problem of predicting spatio-temporal processes with temporalpatterns that vary across spatial regions, when data is obtained as a stream.That is, when the training dataset is augmented sequentially. Specifically, wedevelop a localized spatio-temporal covariance model of the process that cancapture spatially varying temporal periodicities in the data. We then apply acovariance-fitting methodology to learn the model parameters which yields apredictor that can be updated sequentially with each new data point. Theproposed method is evaluated using both synthetic and real climate data whichdemonstrate its ability to accurately predict data missing in spatial regionsover time.

Learning Localized Spatio-Temporal Models From Streaming Data