Online Learning with Radial Basis Function Networks

Gabriel Borrageiro, Nikan Firoozye and Paolo Barucca

Papers from arXiv.org

Abstract: 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.

Date: 2021-03, Revised 2022-10
New Economics Papers: this item is included in nep-big and nep-for
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