Online Learning with Radial Basis Function Networks

03/15/2021
by   Gabriel Borrageiro, et al.
1

We investigate the benefits of feature selection, nonlinear modelling and online learning when forecasting in financial time series. We consider the sequential and continual learning sub-genres of online learning. The experiments we conduct show that there is a benefit to online transfer learning, in the form of radial basis function networks, beyond the sequential updating of recursive least-squares models. We show that the radial basis function networks, which make use of clustering algorithms to construct a kernel Gram matrix, are more beneficial than treating each training vector as separate basis functions, as occurs with kernel Ridge regression. We demonstrate quantitative procedures to determine the very structure of the radial basis function networks. Finally, we conduct experiments on the log returns of financial time series and show that the online learning models, particularly the radial basis function networks, are able to outperform a random walk baseline, whereas the offline learning models struggle to do so.

READ FULL TEXT

page 1

page 2

page 3

page 4

01/22/2019

An Exact Reformulation of Feature-Vector-based Radial-Basis-Function Networks for Graph-based Observations

Radial-basis-function networks are traditionally defined for sets of vec...
03/26/2021

Multi-source Transfer Learning with Ensemble for Financial Time Series Forecasting

Although transfer learning is proven to be effective in computer vision ...
08/19/2022

ACO based Adaptive RBFN Control for Robot Manipulators

This paper describes a new approach for approximating the inverse kinema...
10/07/2021

Time Series Forecasting Using Manifold Learning

We address a three-tier numerical framework based on manifold learning f...
06/09/2021

Multi-layered Network Exploration via Random Walks: From Offline Optimization to Online Learning

Multi-layered network exploration (MuLaNE) problem is an important probl...
08/20/2017

Boltzmann machines for time-series

We review Boltzmann machines extended for time-series. These models ofte...
04/21/2021

Mixture Models for the Analysis, Edition, and Synthesis of Continuous Time Series

This chapter presents an overview of techniques used for the analysis, e...