Neural networks and AdaBoost algorithm based ensemble models for enhanced forecasting of nonlinear time series

摘要

In this paper an optimized AdaBoost Regression and Threshold (AdaBoostRT) algorithm based on feed-forward neural networks is evaluated. The AdaBoostRT algorithm is used to combine an ensemble of feed-forward neural networks trained by using backpropagation algorithm (FFN-BP). The ensemble model is validated by using two typical time-series data, namely Chua's circuit and CATS benchmark data. The performance of the ensemble models is shown to outperform several existing approaches.