Predicting with Variables Constructed from Temporal Sequences

Mehmet Kayaalp, Gregory F. Cooper, Gilles Clermont

AISTATS 2001 pp. 143-148

/aistats/2001/kayaalp2001aistats-predicting/

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.

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Text

Kayaalp et al. "Predicting with Variables Constructed from Temporal Sequences." Proceedings of the Eighth International Workshop on Artificial Intelligence and Statistics, 2001.

Markdown

[Kayaalp et al. "Predicting with Variables Constructed from Temporal Sequences." Proceedings of the Eighth International Workshop on Artificial Intelligence and Statistics, 2001.](https://mlanthology.org/aistats/2001/kayaalp2001aistats-predicting/)

BibTeX

@inproceedings{kayaalp2001aistats-predicting,
  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},
  year      = {2001},
  pages     = {143-148},
  volume    = {R3},
  url       = {https://mlanthology.org/aistats/2001/kayaalp2001aistats-predicting/}
}