On-device research index

arXiv research

A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.

168,742 papers · 148 categories

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5 results for “TimeGAN”

Synthetic Financial Data Generation for Enhanced Financial Modellingcs.LG

Unified framework evaluates synthetic financial data models.

problem Data scarcity and confidentiality in finance hinder model development and testing.

method Multi-criteria evaluation of three generative paradigms: ARIMA-GARCH, VAEs, and TimeGAN.

result TimeGAN achieves the best balance between realism and temporal coherence.

synthetic data financial modeling ARIMA-GARCH VAEs TimeGAN

2025-12-25abs ↗pdf ↗

Privacy-Preserving Generative Modeling and Clinical Validation of Longitudinal Health Records for Chronic Diseasecs.LG

Enhances generative model for clinical data privacy and accuracy.

problem Data privacy in electronic patient records.

method Improves a time-series generative model with privacy safeguards.

result DP-TimeGAN achieves a mean authenticity of 0.778 on the CKD dataset.

data privacy chronic disease longitudinal health records generative models machine learning

2025-11-29abs ↗pdf ↗

Deep Generative Models for Synthetic Financial Data: Applications to Portfolio and Risk Modelingq-fin.ST

This paper uses deep generative models to create synthetic financial data for portfolio and risk modeling.

problem Challenges in empirical research due to privacy, accessibility, and reproducibility issues in financial data.

method Investigates the use of Time-series Generative Adversarial Networks (TimeGAN) and Variational Autoencoders (VAEs) to generate synthetic financial return series.

result Synthetic data from TimeGAN closely mimics real financial data in distributional shapes, volatility, and autocorrelation.

deep generative models synthetic financial data portfolio optimization risk modeling time-series analysis

2025-12-25abs ↗pdf ↗

A novel approach to rating transition modelling via Machine Learning and SDEs on Lie groupsq-fin.RM

A novel approach models rating transitions using Lie groups and Deep Learning.

problem Modeling rating transitions with geometric properties and stochastic processes.

method Introducing Itô-SDEs on Lie groups, using TimeGAN for calibration, and examining rating matrix properties.

result The geometric approach using Lie groups and Deep Learning generates a good fit for rating transitions.

Machine Learning Deep Learning SDEs Lie groups Rating transitions

2022-05-31abs ↗pdf ↗

Improving Machine Learning Performance with Synthetic Augmentationcs.AI

Synthetic augmentation improves financial machine learning performance in variance-dominant regimes.

problem Data scarcity in financial machine learning.

method Formalized synthetic augmentation, introduced size-matched null augmentation, and developed a non-parametric block permutation test.

result Synthetic augmentation is beneficial only in variance-dominant regimes, such as persistent volatility forecasting.

synthetic augmentation financial machine learning data scarcity Markov-switching environments null augmentation

2026-04-16abs ↗pdf ↗