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Learning Localized Spatio-Temporal Models From Streaming Data
Muhammad Osama · Dave Zachariah · Thomas Schön

Thu Jul 12 09:15 AM -- 12:00 PM (PDT) @ Hall B #143

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.

Author Information

Muhammad Osama (Uppsala University)
Dave Zachariah (Uppsala University)
Thomas Schön (Uppsala University)

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