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• W3138187898 abstract "We investigate the benefits of feature selection, nonlinear modelling and online learning when forecasting financial time series. We combine sequential updating with continual learning, specifically transfer learning. We perform feature representation transfer through clustering algorithms that determine the analytical structure of radial basis function networks we construct. These networks achieve lower mean-square prediction errors than kernel ridge regression models, which arbitrarily use all training vectors as prototypes. We also demonstrate quantitative procedures to determine the very structure of the networks. Finally, we conduct experiments on the log-returns of financial time series and show that these online transfer learning models outperform a random-walk baseline. In contrast, the offline learning models struggle to do so." @default.
• W3138187898 created "2021-03-29" @default.
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• W3138187898 date "2021-12-28" @default.
• W3138187898 modified "2023-09-27" @default.
• W3138187898 title "Online Learning with Radial Basis Function Networks" @default.
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• W3138187898 doi "https://doi.org/10.36227/techrxiv.14851077" @default.
• W3138187898 hasPublicationYear "2021" @default.
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