# Multiple Yield Curve Modeling and Forecasting using Deep Learning

arXiv: https://arxiv.org/abs/2401.16985 · HTML: https://mlsanity.com/paper/2401.16985/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 48 references that are in the map.
- First posted on arXiv 30 Jan 2024.

## Builds on (most cited first, 5 of 5)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles](https://mlsanity.com/paper/1612.01474/) (2016)
- [Entity Embeddings of Categorical Variables](https://mlsanity.com/paper/1604.06737/) (2016)
- [LocalGLMnet: interpretable deep learning for tabular data](https://mlsanity.com/paper/2107.11059/) (2021)
- [Calibrating the Lee-Carter and the Poisson Lee-Carter models via Neural Networks](https://mlsanity.com/paper/2106.12312/) (2021)

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

- [Arbitrage-Free Bond and Yield Curve Forecasting with Neural Filters under HJM Constraints](https://mlsanity.com/paper/2511.17892/) (2025)

## Abstract (arXiv)

This manuscript introduces deep learning models that simultaneously describe the dynamics of several yield curves. We aim to learn the dependence structure among the different yield curves induced by the globalization of financial markets and exploit it to produce more accurate forecasts. By combining the self-attention mechanism and nonparametric quantile regression, our model generates both point and interval forecasts of future yields. The architecture is designed to avoid quantile crossing issues affecting multiple quantile regression models. Numerical experiments conducted on two different datasets confirm the effectiveness of our approach. Finally, we explore potential extensions and enhancements by incorporating deep ensemble methods and transfer learning mechanisms.