Deep Generators on Commodity Markets; application to Deep Hedging

no code implementations • 27 May 2022 • Nicolas Boursin, Carl Remlinger, Joseph Mikael, Carol Anne Hargreaves

Driven by the good results obtained in computer vision, deep generative methods for time series have been the subject of particular attention in recent years, particularly from the financial industry.

Generative Adversarial Network Time Series +1

Expert Aggregation for Financial Forecasting

no code implementations • 25 Nov 2021 • Carl Remlinger, Brière Marie, Alasseur Clémence, Joseph Mikael

Machine learning algorithms dedicated to financial time series forecasting have gained a lot of interest.

BIG-bench Machine Learning Time Series +1

Conditional Loss and Deep Euler Scheme for Time Series Generation

no code implementations • 10 Feb 2021 • Carl Remlinger, Joseph Mikael, Romuald Elie

We introduce three new generative models for time series that are based on Euler discretization of Stochastic Differential Equations (SDEs) and Wasserstein metrics.

Time Series Time Series Analysis +2

An Euler-based GAN for time series

no code implementations • 1 Jan 2021 • Carl Remlinger, Joseph Mickael, Romuald Elie

A new model of generative adversarial networks for time series based on Euler scheme and Wasserstein distances including Sinkhorn divergence is proposed.

Time Series Time Series Analysis +1

Reinforcement Learning in Economics and Finance

no code implementations • 22 Mar 2020 • Arthur Charpentier, Romuald Elie, Carl Remlinger

As in multi-armed bandit problems, when an agent picks an action, he can not infer ex-post the rewards induced by other action choices.

reinforcement-learning Reinforcement Learning (RL)