Performance improvement via bagging in ensemble prediction of chaotic time series using similarity of attractors and LOOCV predictable horizon

Abstract

Recently, we have presented a method of ensemble prediction of chaotic time series. The method employs strong learners capable of making predictions with small error and usual ensemble mean does not work for long term prediction owing to the long term unpredictability of chaotic time series. Thus, the method uses similarity of attractors to select plausible predictions from original predictions generated by strong leaners, and then calculates LOOCV (leave-one-out crossvalidation) measure to estimate predictable horizons. Finally, it provides representative prediction and an estimation of the predictable horizon. We have used CAN2s (competitive associative nets) for learning piecewise linear approximation of nonlinear function as strong learners in the previous study, and this paper employs bagging of them to improve the performance, and shows the validity and the effectiveness of the method.