Learning Localized Spatio-Temporal Models From Streaming Data
This addresses incremental improvements in streaming spatio-temporal prediction for applications like climate modeling.
The paper tackles the problem of predicting spatio-temporal processes with regionally varying temporal patterns from streaming data, developing a localized covariance model that captures spatially varying periodicities and can be updated sequentially. Results show accurate prediction of missing data in spatial regions over time, demonstrated on synthetic and real climate data.
We address the problem of predicting spatio-temporal processes with temporal patterns that vary across spatial regions, when data is obtained as a stream. That is, when the training dataset is augmented sequentially. Specifically, we develop a localized spatio-temporal covariance model of the process that can capture spatially varying temporal periodicities in the data. We then apply a covariance-fitting methodology to learn the model parameters which yields a predictor that can be updated sequentially with each new data point. The proposed method is evaluated using both synthetic and real climate data which demonstrate its ability to accurately predict data missing in spatial regions over time.