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Neural Computing and Applications

Erschienen in:

01.04.2004 | Original Article

Multiple neural networks for a long term time series forecast

verfasst von: Hanh H. Nguyen, Christine W. Chan

Erschienen in: Neural Computing and Applications | Ausgabe 1/2004

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Abstract

The artificial neural network (ANN) methodology has been used in various time series prediction applications. However, the accuracy of a neural network model may be seriously compromised when it is used recursively for making long-term multi-step predictions. This study presents a method using multiple ANNs to make a long term time series prediction. A multiple neural network (MNN) model is a group of neural networks that work together to solve a problem. In the proposed MNN approach, each component neural network makes forecasts at a different length of time ahead. The MNN method was applied to the problem of forecasting an hourly customer demand for gas at a compression station in Saskatchewan, Canada. The results showed that a MNN model performed better than a single ANN model for long term prediction.

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Titel: Multiple neural networks for a long term time series forecast
verfasst von: Hanh H. Nguyen
Christine W. Chan
Publikationsdatum: 01.04.2004
Verlag: Springer-Verlag
Erschienen in: Neural Computing and Applications / Ausgabe 1/2004
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI: https://doi.org/10.1007/s00521-003-0390-z

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