A novel nonlinear neural network ensemble model for financial time series forecasting

Authors:
Kin Keung Lai;Lean Yu;Shouyang Wang;Huang Wei
Affiliations:
College of Business Administration, Hunan University, Changsha, China;Department of Management Sciences, City University of Hong Kong, Kowloon, Hong Kong;College of Business Administration, Hunan University, Changsha, China;School of Management, Huazhong University of Science and Technology, Wuhan, China
Venue:
ICCS'06 Proceedings of the 6th international conference on Computational Science - Volume Part I
Year:
2006

Citing 4
Cited 5

The nature of statistical learning theory

The nature of statistical learning theory
Neural Networks for Pattern Recognition

Neural Networks for Pattern Recognition
A novel nonlinear ensemble forecasting model incorporating GLAR and ANN for foreign exchange rates

Computers and Operations Research
Parallel consensual neural networks

IEEE Transactions on Neural Networks

An Effective Hybrid GA---PP Strategy for Artificial Neural Network Ensemble and Its Application Stock Market Forecasting

ISNN 2009 Proceedings of the 6th International Symposium on Neural Networks: Advances in Neural Networks - Part II
Design of ensemble neural network using entropy theory

Advances in Engineering Software
Multistage neural network metalearning with application to foreign exchange rates forecasting

MICAI'06 Proceedings of the 5th Mexican international conference on Artificial Intelligence
Forecast combination strategies for handling structural breaks for time series forecasting

MCS'10 Proceedings of the 9th international conference on Multiple Classifier Systems
Long-memory time series ensembles for concept shift detection

Proceedings of the 2nd International Workshop on Big Data, Streams and Heterogeneous Source Mining: Algorithms, Systems, Programming Models and Applications

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this study, a new nonlinear neural network ensemble model is proposed for financial time series forecasting. In this model, many different neural network models are first generated. Then the principal component analysis technique is used to select the appropriate ensemble members. Finally, the support vector machine regression method is used for neural network ensemble. For further illustration, two real financial time series are used for testing.