  Financial time series are characterised by their nonstationarity and
autocorrelation. Even if these time series are differenced, technically
ensuring their stationarity, they experience regular covariate shifts and
concept drifts. Against this backdrop, we combine feature representation
transfer with sequential optimisation to provide multi-horizon returns
forecasts. Our online learning rbfnet outperforms a random-walk baseline and
several powerful batch learners. The rbfnets we formulate are naturally
designed to measure the similarity between test samples and continuously
updated prototypes that capture the characteristics of the feature space.
