  We investigate the benefits of feature selection, nonlinear modelling and
online learning with forecasting in financial time series. We consider the
sequential and continual learning sub-genres of online learning. Through
empirical experimentation, which involves long term forecasting in daily
sampled cross-asset futures, and short term forecasting in minutely sampled
cash currency pairs, we find that the online learning techniques outperform the
offline learning ones. 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.
