Before revision:  We also find that , in the subset of models we use , sequential learning in time with online Ridge regression, provides the best next step ahead forecasts, and continual learning with an online radial basis function network, provides the best multi-step ahead forecasts. We combine the benefits of both in a precision weighted ensemble of the forecast errors and find superior forecast performance overall .
After revision:  We also find that , in the subset of models we use , sequential learning in time with online Ridge regression, provides the best next step ahead forecasts, and continual learning with an online radial basis function networks, are able to outperform a random walk baseline, whereas the offline learning models struggle to do so .