Selective fusion of heterogeneous classifiers
Intelligent Data Analysis
Adaptive mixtures of local experts
Neural Computation
A Comparison of Hybrid ARMA-Elman Models with Single Models for Forecasting Interest Rates
IITA '08 Proceedings of the 2008 Second International Symposium on Intelligent Information Technology Application - Volume 02
Feature subset selection in large dimensionality domains
Pattern Recognition
Generalized regression neural network in modelling river sediment yield
Advances in Engineering Software
Predicting stock returns by classifier ensembles
Applied Soft Computing
ICONIP'06 Proceedings of the 13th international conference on Neural Information Processing - Volume Part II
Stock time series forecasting using support vector machines employing analyst recommendations
ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part III
A general regression neural network
IEEE Transactions on Neural Networks
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We propose a novel homogeneous neural network ensemble approach called Generalized Regression Neural Network (GEFTS-GRNN) Ensemble for Forecasting Time Series, which is a concatenation of existing machine learning algorithms. GEFTS uses a dynamic nonlinear weighting system wherein the outputs from several base-level GRNNs are combined using a combiner GRNN to produce the final output. We compare GEFTS with the 11 most used algorithms on 30 real datasets. The proposed algorithm appears to be more powerful than existing ones. Unlike conventional algorithms, GEFTS is effective in forecasting time series with seasonal patterns.