PT - JOURNAL ARTICLE AU - Gabriel Borrageiro AU - Nick Firoozye AU - Paolo Barucca TI - Online Learning with Radial Basis Function Networks AID - 10.3905/jfds.2022.1.112 DP - 2023 Jan 31 TA - The Journal of Financial Data Science PG - 76--95 VI - 5 IP - 1 4099 - https://pm-research.com/content/5/1/76.short 4100 - https://pm-research.com/content/5/1/76.full 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.