Online Learning with Radial Basis Function Networks

• URL: http://arxiv.org/abs/2103.08414v1
• Date: Mon, 15 Mar 2021 14:39:40 GMT
• Title: Online Learning with Radial Basis Function Networks
• Authors: Gabriel Borrageiro, Nick Firoozye and Paolo Barucca
• Abstract summary: We consider the sequential and continual learning sub-genres of online learning. We find that the online learning techniques outperform the offline learning ones.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We investigate the benefits of feature selection, nonlinear modelling and online learning with forecasting in financial time series. We consider the sequential and continual learning sub-genres of online learning. Through empirical experimentation, which involves long term forecasting in daily sampled cross-asset futures, and short term forecasting in minutely sampled cash currency pairs, we find that the online learning techniques outperform the offline learning ones. We also find that, in the subset of models we use, sequential learning in time with online Ridge regression, provides the best next step ahead forecasts, and continual learning with an online radial basis function network, provides the best multi-step ahead forecasts. We combine the benefits of both in a precision weighted ensemble of the forecast errors and find superior forecast performance overall.

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