Predicting with Variables Constructed from Temporal Sequences

Mehmet Kayaalp, Gregory F. Cooper, Gilles Clermont
Proceedings of the Eighth International Workshop on Artificial Intelligence and Statistics, PMLR R3:143-148, 2001.

Abstract

In this study, we applied the local learning paradigm and conditional independence assumptions to control the rapid growth of the dimensionality introduced by multivariate time series. We also combined various univariate time series with different stationary assumptions in temporal models. These techniques are applied to learn simple Bayesian networks from temporal data and to predict survival probabilities of ICU patients on every day of their ICU stay.

Cite this Paper

BibTeX
@InProceedings{pmlr-vR3-kayaalp01a, title = {Predicting with Variables Constructed from Temporal Sequences}, author = {Kayaalp, Mehmet and Cooper, Gregory F. and Clermont, Gilles}, booktitle = {Proceedings of the Eighth International Workshop on Artificial Intelligence and Statistics}, pages = {143--148}, year = {2001}, editor = {Richardson, Thomas S. and Jaakkola, Tommi S.}, volume = {R3}, series = {Proceedings of Machine Learning Research}, month = {04--07 Jan}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/r3/kayaalp01a/kayaalp01a.pdf}, url = {https://proceedings.mlr.press/r3/kayaalp01a.html}, abstract = {In this study, we applied the local learning paradigm and conditional independence assumptions to control the rapid growth of the dimensionality introduced by multivariate time series. We also combined various univariate time series with different stationary assumptions in temporal models. These techniques are applied to learn simple Bayesian networks from temporal data and to predict survival probabilities of ICU patients on every day of their ICU stay.}, note = {Reissued by PMLR on 31 March 2021.} }
Endnote
%0 Conference Paper %T Predicting with Variables Constructed from Temporal Sequences %A Mehmet Kayaalp %A Gregory F. Cooper %A Gilles Clermont %B Proceedings of the Eighth International Workshop on Artificial Intelligence and Statistics %C Proceedings of Machine Learning Research %D 2001 %E Thomas S. Richardson %E Tommi S. Jaakkola %F pmlr-vR3-kayaalp01a %I PMLR %P 143--148 %U https://proceedings.mlr.press/r3/kayaalp01a.html %V R3 %X In this study, we applied the local learning paradigm and conditional independence assumptions to control the rapid growth of the dimensionality introduced by multivariate time series. We also combined various univariate time series with different stationary assumptions in temporal models. These techniques are applied to learn simple Bayesian networks from temporal data and to predict survival probabilities of ICU patients on every day of their ICU stay. %Z Reissued by PMLR on 31 March 2021.
APA
Kayaalp, M., Cooper, G.F. & Clermont, G.. (2001). Predicting with Variables Constructed from Temporal Sequences. Proceedings of the Eighth International Workshop on Artificial Intelligence and Statistics, in Proceedings of Machine Learning Research R3:143-148 Available from https://proceedings.mlr.press/r3/kayaalp01a.html. Reissued by PMLR on 31 March 2021.

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