Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Ensembling neural networks: many could be better than all
Artificial Intelligence
A novel nonlinear ensemble forecasting model incorporating GLAR and ANN for foreign exchange rates
Computers and Operations Research
A Projection Pursuit Algorithm for Exploratory Data Analysis
IEEE Transactions on Computers
Integrating GA-based time-scale feature extractions with SVMs for stock index forecasting
Expert Systems with Applications: An International Journal
CIS '09 Proceedings of the 2009 International Conference on Computational Intelligence and Security - Volume 01
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In this study, a novel Neural Network (NN) ensemble model using Projection Pursuit Regression (PPR) and Least Squares Support Vector Regression (LS---SVR) is developed for financial forecasting. In the process of ensemble modeling, the first stage some important economic factors are selected by the PPR technology as input feature for NN. In the second stage, the initial data set is divided into different training sets by used Bagging and Boosting technology. In the third stage, these training sets are input to the different individual NN models, and then various single NN predictors are produced based on diversity principle. In the fourth stage, the Partial Least Square (PLS) technology is used to choosing the appropriate number of neural network ensemble members. In the final stage, LS---SVR is used for ensemble of the NN to prediction purpose. For testing purposes, this study compare the new ensemble model's performance with some existing neural network ensemble approaches in terms of the Shanghai Stock Exchange index. Experimental results reveal that the predictions using the proposed approach are consistently better than those obtained using the other methods presented in this study in terms of the same measurements.