A novel neural network ensemble architecture for time series forecasting

被引：52

作者：

Gheyas, Iffat A. ^{[1
]}

Smith, Leslie S. ^{[1
]}

机构：

[1] Univ Aberdeen, Sch Business, Old Aberdeen AB24 3QY, Scotland

来源：

NEUROCOMPUTING | 2011年 / 74卷 / 18期

关键词：

Time series forecasting; Generalized regression neural networks; Neural network ensemble; Curse of dimensionality; Deseasonalization; Dynamic nonlinear weighted voting; PREDICTION;

D O I：

10.1016/j.neucom.2011.08.005

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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. (C) 2011 Elsevier B.V. All rights reserved.

引用

页码：3855 / 3864

页数：10

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