Error bounds for data-driven models of dynamical systems

Comput Biol Med. 2007 May;37(5):670-9. doi: 10.1016/j.compbiomed.2006.06.005. Epub 2006 Aug 8.


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.

Publication types

  • Research Support, U.S. Gov't, Non-P.H.S.

MeSH terms

  • Algorithms*
  • Body Temperature / physiology
  • Computer Simulation*
  • Confidence Intervals
  • Exercise Test
  • Forecasting
  • Humans
  • Models, Biological*
  • Models, Theoretical
  • Neural Networks, Computer
  • Probability
  • Regression Analysis
  • Reproducibility of Results
  • Statistical Distributions
  • Thermometers
  • Walking / physiology