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. © Springer-Verlag Berlin Heidelberg 2006.
CITATION STYLE
Haraki, D., Suzuki, T., & Ikeguchi, T. (2006). Bootstrap prediction intervals for nonlinear time-series. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4224 LNCS, pp. 155–162). Springer Verlag. https://doi.org/10.1007/11875581_19
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