Learning Localized Spatio-Temporal Models From Streaming Data

Muhammad Osama, Dave Zachariah, Thomas Schön
Proceedings of the 35th International Conference on Machine Learning, PMLR 80:3927-3935, 2018.

Abstract

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.

Cite this Paper

BibTeX
@InProceedings{pmlr-v80-osama18a, title = {Learning Localized Spatio-Temporal Models From Streaming Data}, author = {Osama, Muhammad and Zachariah, Dave and Sch{\"o}n, Thomas}, booktitle = {Proceedings of the 35th International Conference on Machine Learning}, pages = {3927--3935}, year = {2018}, editor = {Dy, Jennifer and Krause, Andreas}, volume = {80}, series = {Proceedings of Machine Learning Research}, month = {10--15 Jul}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v80/osama18a/osama18a.pdf}, url = {https://proceedings.mlr.press/v80/osama18a.html}, abstract = {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.} }
Endnote
%0 Conference Paper %T Learning Localized Spatio-Temporal Models From Streaming Data %A Muhammad Osama %A Dave Zachariah %A Thomas Schön %B Proceedings of the 35th International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2018 %E Jennifer Dy %E Andreas Krause %F pmlr-v80-osama18a %I PMLR %P 3927--3935 %U https://proceedings.mlr.press/v80/osama18a.html %V 80 %X 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.
APA
Osama, M., Zachariah, D. & Schön, T.. (2018). Learning Localized Spatio-Temporal Models From Streaming Data. Proceedings of the 35th International Conference on Machine Learning, in Proceedings of Machine Learning Research 80:3927-3935 Available from https://proceedings.mlr.press/v80/osama18a.html.

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