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

Comparing Gaussian Processes and Artificial Neural Networks for Forecasting

Authors
Colin Fyfe 0, Tzai Der Wang, Shang Jen Chuang
Corresponding Author
Colin Fyfe
0university of paisley
DOI
https://doi.org/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.
Open Access
This is an open access article distributed under the CC BY-NC license.

Download article (PDF)

Proceedings
9th Joint International Conference on Information Sciences (JCIS-06)
Part of series
Advances in Intelligent Systems Research
Publication Date
October 2006
ISBN
978-90-78677-01-7
ISSN
1951-6851
DOI
https://doi.org/10.2991/jcis.2006.7How to use a DOI?
Open Access
This is an open access article distributed under the CC BY-NC license.

Cite this article

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  - 9th Joint International Conference on Information Sciences (JCIS-06)
PB  - Atlantis Press
SN  - 1951-6851
UR  - https://doi.org/10.2991/jcis.2006.7
DO  - https://doi.org/10.2991/jcis.2006.7
ID  - Fyfe2006/10
ER  -