Title :
Neural networks and AdaBoost algorithm based ensemble models for enhanced forecasting of nonlinear time series
Author :
Yilin Dong ; Jianhua Zhang ; Garibaldi, Jonathan M.
Author_Institution :
Dept. of Autom., East China Univ. of Sci. & Technol., Shanghai, China
Abstract :
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.
Keywords :
feedforward neural nets; learning (artificial intelligence); regression analysis; time series; AdaBoost.RT algorithm; CATS benchmark data; Chua circuit; FFN-BP; backpropagation algorithm; enhanced nonlinear time series forecasting; ensemble models; feed-forward neural networks; optimized AdaBoost regression and threshold algorithm; Benchmark testing; Cats; Integrated circuit modeling; Neural networks; Predictive models; Time series analysis; Training; AdaBoostRT; ensemble learning; feed-forward neural networks; time-series prediction;
Conference_Titel :
Neural Networks (IJCNN), 2014 International Joint Conference on
Conference_Location :
Beijing
Print_ISBN :
978-1-4799-6627-1
DOI :
10.1109/IJCNN.2014.6889364
