Title :
An improved boosting scheme based ensemble of Fuzzy Neural Networks for nonlinear time series prediction
Author :
Yilin Dong ; Jianhua Zhang
Author_Institution :
Dept. of Autom., East China Univ. of Sci. & Technol., Shanghai, China
Abstract :
This paper proposed a Modified AdaBoostRT (AdaBoost Regression and Threshold) algorithm based on Fuzzy Neural Networks (FNNs) and its application to the accurate prediction of complex nonlinear time-series. The algorithm is validated by using four typical time-series data, namely Lorenz, Mackey-Glass, Sunspot and Dow Jones Indices data. The performance comparison of the proposed method and several existing approaches is also performed to show its advantages for nonlinear time series prediction problems.
Keywords :
data analysis; fuzzy neural nets; learning (artificial intelligence); time series; Dow Jones Indices data; FNN; Lorenz data; Mackey-Glass data; Sunspot data; complex nonlinear time-series prediction; fuzzy neural network ensemble; improved boosting scheme; modified AdaBoost regression and threshold algorithm; modified AdaBoostRT algorithm; time-series data; Boosting; Classification algorithms; Equations; Fuzzy neural networks; Mathematical model; Prediction algorithms; Time series analysis; Modified AdaBoostRT; ensemble learning; fuzzy 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.2014.6889431
