Comparing Gaussian Processes and Artificial Neural Networks for Forecasting

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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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  -