|Appears in Collections:||Computing Science and Mathematics Journal Articles|
|Peer Review Status:||Refereed|
|Title:||A Novel Neural Network Ensemble Architecture for Time Series Forecasting|
|Authors:||Gheyas, Iffat A|
|Keywords:||Time series forecasting|
Generalized regression neural networks
Neural network ensemble
Curse of dimensionality
Dynamic nonlinear weighted voting
|Citation:||Gheyas IA & Smith L (2011) A Novel Neural Network Ensemble Architecture for Time Series Forecasting, Neurocomputing, 74 (18), pp. 3855-3864.|
|Abstract:||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 use 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.|
|Rights:||The publisher does not allow this work to be made publicly available in this Repository. Please use the Request a Copy feature at the foot of the Repository record to request a copy directly from the author; you can only request a copy if you wish to use this work for your own research or private study.|
|Affiliation:||University of Stirling|
Computing Science - CSM Dept
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