Proceedings of the 9th Joint International Conference on Information Sciences (JCIS-06)

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Comparing Gaussian Processes and Artificial Neural Networks for Forecasting

Authors
Colin Fyfe1, Tzai Der Wang, Shang Jen Chuang
1university of paisley
Corresponding Author
Colin Fyfe
Available Online October 2006.
DOI
10.2991/jcis.2006.7How to use a DOI?
Keywords
Gaussian processes, supervised learning, prediction
Abstract

We compare the use of artificial neural networks and Gaussian processes for forecasting. We show that Artificial Neural Networks have the advantage of being utilisable with greater volumes of data but Gaussian processes can more easily be utilised to deal with non-stationarity.

Copyright
© 2006, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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Volume Title
Proceedings of the 9th Joint International Conference on Information Sciences (JCIS-06)
Series
Advances in Intelligent Systems Research
Publication Date
October 2006
ISBN
10.2991/jcis.2006.7
ISSN
1951-6851
DOI
10.2991/jcis.2006.7How to use a DOI?
Copyright
© 2006, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Cite this article

risenwbib
TY  - CONF
AU  - Colin Fyfe
AU  - Tzai Der Wang
AU  - Shang Jen Chuang
PY  - 2006/10
DA  - 2006/10
TI  - Comparing Gaussian Processes and Artificial Neural Networks for Forecasting
BT  - Proceedings of the 9th Joint International Conference on Information Sciences (JCIS-06)
PB  - Atlantis Press
SP  - 29
EP  - 32
SN  - 1951-6851
UR  - https://doi.org/10.2991/jcis.2006.7
DO  - 10.2991/jcis.2006.7
ID  - Fyfe2006/10
ER  -