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.
Copyright
© The authors. This article is distributed under the terms of the Creative Commons Attribution License 4.0, which permits non-commercial use, distribution and reproduction in any medium, provided the original work is properly cited. See for details: https://creativecommons.org/licenses/by-nc/4.0/
Open Access | Under Creative Commons license CC BY-NC 4.0

Download article (PDF)

Cite this article
ris
enw
bib
@inproceedings{Fyfe2006,
  title={Comparing Gaussian Processes and Artificial Neural Networks for Forecasting},
  author={Fyfe, Colin and Wang, Tzai Der and Chuang, Shang Jen},
  year={2006},
  booktitle={9th Joint International Conference on Information Sciences (JCIS-06)},
  issn={1951-6851},
  isbn={978-90-78677-01-7},
  url={http://dx.doi.org/10.2991/jcis.2006.7},
  doi={10.2991/jcis.2006.7},
  publisher={Atlantis Press}
}
copy to clipboarddownload