UBC Neurological Network prediction     Statistical Model

University of British Columbia
Victoria, BC, Canada

In this neurological network prediction system, the predictors are monthly sea level pressure, and monthly extended reconstructed sea surface temperature. Bayesian regularization is used in the neurological network training, where the optimal weight penalty parameter in the cost function is estimated by a Bayesian approach

View the current neurological network forecasts.

Browse the UBC Prediction Group's web page.

Contact: Aiming Wu: awu@eos.ubc.ca

References:
Tangang, F.T., W.W. Hsieh and B. Tang, 1997: Forecasting the equatorial Pacific sea surface temperatures by neural network models. Climate Dyn., 13, 135-147.
Tang, B., W.W. Hsieh, A.H. Monahan and F.T. Tangang, 2000. Skill comparisons between neural networks and canonical correlation analysis in predicting the equatorial Pacific sea surface temperatures. J. Climate, 13, 287-293.
Wu, A., W.W. Hsieh and B. Tang, 2006. Neural network forecasts of the tropical Pacific sea surface temperatures. Neural Networks, 19, 145-154. doi:10.1016/j.neunet.2006.01.004.