Bootstrap Prediction Intervals for Nonlinear Time-Series

  • Daisuke Haraki
  • Tomoya Suzuki
  • Tohru Ikeguchi
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4224)


To evaluate predictability of complex behavior produced from nonlinear dynamical systems, we often use normalized root mean square error, which is suitable to evaluate errors between true points and predicted points. However, it is also important to estimate prediction intervals, where the future point will be included. Although estimation of prediction intervals is conventionally realized by an ensemble prediction, we applied the bootstrap resampling scheme to evaluate prediction intervals of nonlinear time-series. By several numerical simulations, we show that the bootstrap method is effective to estimate prediction intervals for nonlinear time-series.


Bootstrap Method Prediction Interval Ensemble Prediction Prediction Step Chaotic Time Series 
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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Daisuke Haraki
    • 1
  • Tomoya Suzuki
    • 2
  • Tohru Ikeguchi
    • 1
  1. 1.Graduate School of Science and EngineeringSaitama UniversitySaitama-cityJapan
  2. 2.Department of Information Systems DesignDoshisya UniversityKyotanabe-cityJapan

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