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

fYear

2014

fDate

6-11 July 2014

Firstpage

157

Lastpage

164

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;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks (IJCNN), 2014 International Joint Conference on

Conference_Location

Beijing

Print_ISBN

978-1-4799-6627-1

Type

conf

DOI

10.1109/IJCNN.2014.6889431

Filename

6889431