TY - JOUR T1 - Online Learning with Radial Basis Function Networks JF - The Journal of Financial Data Science SP - 76 LP - 95 DO - 10.3905/jfds.2022.1.112 VL - 5 IS - 1 AU - Gabriel Borrageiro AU - Nick Firoozye AU - Paolo Barucca Y1 - 2023/01/31 UR - https://pm-research.com/content/5/1/76.abstract N2 - 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. ER -