A novel neural network ensemble architecture for time series forecasting

Iffat A. Gheyas, Leslie S. Smith

Research output: Contribution to journalArticlepeer-review

63 Citations (Scopus)


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.
Original languageEnglish
Pages (from-to)3855-3864
Number of pages10
Issue number18
Publication statusPublished - Nov 2011
Externally publishedYes


Dive into the research topics of 'A novel neural network ensemble architecture for time series forecasting'. Together they form a unique fingerprint.

Cite this