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
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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.
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Proceedings
9th Joint International Conference on Information Sciences (JCIS-06)
Publication Date
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ISBN
978-90-78677-01-7
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  - NaN/NaN
DA  - NaN/NaN
TI  - Comparing Gaussian Processes and Artificial Neural Networks for Forecasting
BT  - 9th Joint International Conference on Information Sciences (JCIS-06)
PB  - Atlantis Press
UR  - https://doi.org/10.2991/jcis.2006.7
DO  - https://doi.org/10.2991/jcis.2006.7
ID  - FyfeNaN/NaN
ER  -