Time series forecasting by combining RBF networks, certainty factors, and the Box-Jenkins model
NeurocomputingPublished 1 March 1996
Donald K. Wedding, Krzysztof J. Cios
Citations144
SJR quartileQ1
SJR score1.47
SNIP1.94
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TL;DR
The RBF neural networks are trained to generate both time series forecasts and certainty factors and their output is combined with the Univariant Box-Jenkins models to predict future values of data.
Abstract
A method is described for using Radial Basis Function (RBF) neural networks to generate certainty factors along with normal output. When RBF output with low certainty factors values are discarded, the overall accuracy of the network is increased. In this paper, RBF networks are used in a time series application. The RBF neural networks are trained to generate both time series forecasts and certainty factors. Their output is then combined with the Univariant Box-Jenkins (UBJ) models to predict future values of data. This combination approach is shown to improve the overall reliability of time series forecasting. Three possible methods for combining the two forecasts into one hybrid forecast are discussed.
Keywords
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