Comparing Gaussian Processes and Artificial Neural Networks for Forecasting
- Colin Fyfe 0, Tzai Der Wang, Shang Jen Chuang
- Corresponding Author
- Colin Fyfe
0university of paisley
- https://doi.org/10.2991/jcis.2006.7How to use a DOI?
- Gaussian processes, supervised learning, prediction
- 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.
- © The authors.
- Open Access
- This is an open access article distributed under the CC BY-NC license.
Cite this article
TY - CONF AU - Fyfe, Colin AU - Wang, Tzai Der AU - Chuang, Shang Jen DA - 2006/10/05 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 ER -