# Online Learning with Radial Basis Function Networks

arXiv: https://arxiv.org/abs/2103.08414 · HTML: https://mlsanity.com/paper/2103.08414/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 103 references that are in the map.
- First posted on arXiv 15 Mar 2021.

## Builds on (most cited first, 6 of 6)

- [Overcoming catastrophic forgetting in neural networks](https://mlsanity.com/paper/1612.00796/) (2016)
- [Auto-Encoding Variational Bayes](https://mlsanity.com/paper/1312.6114/) (2013)
- [Learning to reinforcement learn](https://mlsanity.com/paper/1611.05763/) (2016)
- [Continual Reinforcement Learning with Complex Synapses](https://mlsanity.com/paper/1802.07239/) (2018)
- [QuantNet: Transferring Learning Across Systematic Trading Strategies](https://mlsanity.com/paper/2004.03445/) (2020)
- [Second-Order Non-Stationary Online Learning for Regression](https://mlsanity.com/paper/1303.0140/) (2013)

## Built on by (most cited first, 1 of 1)

- [Reinforcement Learning for Systematic FX Trading](https://mlsanity.com/paper/2110.04745/) (2021)

## Abstract (arXiv)

Financial time series are characterised by their nonstationarity and autocorrelation. Even if these time series are differenced, technically ensuring their stationarity, they experience regular covariate shifts and concept drifts. Against this backdrop, we combine feature representation transfer with sequential optimisation to provide multi-horizon returns forecasts. Our online learning rbfnet outperforms a random-walk baseline and several powerful batch learners. The rbfnets we formulate are naturally designed to measure the similarity between test samples and continuously updated prototypes that capture the characteristics of the feature space.