RT Journal Article SR Electronic T1 Online Learning with Radial Basis Function Networks JF The Journal of Financial Data Science FD Institutional Investor Journals SP 76 OP 95 DO 10.3905/jfds.2022.1.112 VO 5 IS 1 A1 Gabriel Borrageiro A1 Nick Firoozye A1 Paolo Barucca YR 2023 UL https://pm-research.com/content/5/1/76.abstract AB The authors provide multi-horizon forecasts on the returns of financial time series. Their sequentially optimised radial basis function network (RBFNet) outperforms a random-walk baseline and several powerful supervised learners. Their RBFNets naturally measure the similarity between test samples and prototypes that capture the characteristics of the feature space. The authors show that the training set financial time series returns have low similarity with their test set counterparts, highlighting the challenges faced in particular by kernel-based methods that use the training set returns as test-time prototypes; in contrast, their online learning RBFNets have hidden units that retain greater similarity across time.