Comparing Gaussian Processes and Artificial Neural Networks for Forecasting
Colin Fyfe 0, Tzai Der Wang, Shang Jen Chuang
0university of paisley
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- 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.
- 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 -