2022
DOI: 10.3905/jfds.2022.1.112
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Online Learning with Radial Basis Function Networks

Abstract: We investigate the benefits of feature selection, nonlinear modelling and online learning when forecasting in financial time series. We consider the sequential and continual learning sub-genres of online learning. The experiments we conduct show that there is a benefit to online transfer learning, in the form of radial basis function networks, beyond the sequential updating of recursive least-squares models. We show that the radial basis function networks, which make use of clustering algorithms to construct a… Show more

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Cited by 3 publications
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