arXiv Machine Learning July 7, 2026 By Fusheng Luo, H'elyette Geman

Yield Curves Dynamics Using Variational Autoencoders Under No-arbitrage

Read the original on arXiv Machine Learning →

arXiv:2605. 12764v3 Announce Type: replace-cross Abstract: This paper introduces a physics-informed generative framework that resolves the fundamental conflict between the statistical flexibility of deep learning and the rigorous theoretical constraints of fixed-income modeling.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

arXiv Machine Learning

Jun 3

FinStressTS: A Parametric Synthetic Benchmark for Time-Series Forecasting in Finance

arXiv:2606. 03184v1 Announce Type: cross Abstract: Financial forecasting is difficult due to low signal-to-noise ratios, latent factors, heavy tails, regime shifts, and jumps.

By Jiaze Sun, Kelvin J. L. Koa, Ruiyang Ni, Yize Liu, Haonan Chen, Ke-Wei Huang

llms multimodal benchmarks safety

arXiv AI

Aug 19

MoFE: A Novel Mixture-of-Experts Framework with Fourier Neural Operators for Cryptocurrency Forecasting

MoFE is a deep learning framework that combines Fourier Neural Operators with a Mixture-of-Experts architecture to forecast cryptocurrency prices. It models volatility as a mix of multi-frequency components—including fundamental growth, mining costs, halving events, and market sentiment—using adaptive FNO and convolutional experts. Experiments on Bitcoin data from 2020 to 2025 show MoFE outperforms existing models in short‑term horizons, reducing phase‑lag errors and improving directional accuracy and information coefficient, which translates into higher Sharpe ratios in simulated trading.

By Bowen Liu, Mingming Sun

benchmarks

arXiv AI

Sep 10

Explainable Deep Learning for Price-Trade Dynamics: From Black-Box Forecasts to Effective Parametric Models

arXiv:2609.06085v1 Announce Type: cross Abstract: Understanding the joint dynamics of prices and trades is central to market microstructure, where returns and order flow interact through nonlinear an...

By Manuel Naviglio, Fabrizio Lillo

benchmarks safety

arXiv Machine Learning

Aug 21

Deep-MKV-TS: Path-Dependent McKean--Vlasov Control for Financial Time Series Generation

arXiv:2608. 19394v1 Announce Type: cross Abstract: We introduce Deep-MKV-TS, a path-dependent McKean-Vlasov framework for financial scenario generation.

By Samer El Boustany, Th\'eo Basseras, Samy Mekkaoui, Alexandre Alouadi, Yadh Hafsi, Huy\^en Pham

arXiv Machine Learning

Aug 10

Embedded Variational Neural Stochastic Differential Equations for Learning Heterogeneous Dynamics

arXiv:2604. 00669v2 Announce Type: replace Abstract: This study examines the challenges of modeling complex and noisy data related to socioeconomic factors over time, with a focus on data from various districts in Odisha, India.

By Sandeep Kumar Samota, Reema Gupta, Snehashish Chakraverty

rag diffusion

arXiv Machine Learning

Jun 15

Neural ARFIMA model for forecasting BRIC exchange rates with long memory

arXiv:2509. 06697v3 Announce Type: replace-cross Abstract: Exchange rate forecasting remains a challenging problem, particularly for emerging economies, where the observed time series exhibit pronounced long-memory dependence, nonlinear dynamics, and sensitivity to macro-financial drivers.

By Donia Besher, Madhurima Panja, Shovon Sengupta, Tanujit Chakraborty

benchmarks

One email a morning, machine-written

Related stories

FinStressTS: A Parametric Synthetic Benchmark for Time-Series Forecasting in Finance

MoFE: A Novel Mixture-of-Experts Framework with Fourier Neural Operators for Cryptocurrency Forecasting

Explainable Deep Learning for Price-Trade Dynamics: From Black-Box Forecasts to Effective Parametric Models

Deep-MKV-TS: Path-Dependent McKean--Vlasov Control for Financial Time Series Generation

Embedded Variational Neural Stochastic Differential Equations for Learning Heterogeneous Dynamics

Neural ARFIMA model for forecasting BRIC exchange rates with long memory