Predicting with Variables Constructed from Temporal Sequences

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.