Error bounds for data-driven models of dynamical systems

  • Authors:

  • Nicholas O. Oleng';Andrei Gribok;Jaques Reifman

  • Affiliations:

  • Bioinformatics Cell, U.S. Army Medical Research and Materiel Command, Frederick, MD 21702, USA;Bioinformatics Cell, U.S. Army Medical Research and Materiel Command, Frederick, MD 21702, USA;Bioinformatics Cell, U.S. Army Medical Research and Materiel Command, Frederick, MD 21702, USA

  • Venue:
  • Computers in Biology and Medicine
  • Year:
  • 2007

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Abstract

This work provides a technique for estimating error bounds about the predictions of data-driven models of dynamical systems. The bootstrap technique is applied to predictions from a set of dynamical system models, rather than from the time-series data, to estimate the reliability (in the form of prediction intervals) for each prediction. The technique is illustrated using human core temperature data, modeled by a hybrid (autoregressive plus first principles) approach. The temperature prediction intervals obtained are in agreement with those from the Camp-Meidell inequality. Moreover, as expected, the prediction intervals increase with the prediction horizon, time-series data variability, and model inaccuracy.