Sign up to save your podcasts
Or
Share Stefan Jansen: Synthetic Data Generation in Finance
Share to email
Share to Facebook
Share to X
Share to email
Share to Facebook
Share to X
Or
Stefan Jansen: Synthetic Data Generation in Finance
Check out the upcoming speakers at: https://qufallschool.splashthat.com/
Subscribe to this podcast at www.anchor.fm/qupodcast
Or
On Apple Podcast at https://podcasts.apple.com/us/podcast/qupodcast/id1510865003
Slides and video at: https://academy.qusandbox.com/#/market/5fb54e3499aa4a24691da86c
A conversation with Quants, Thinkers and Innovators all challenged to innovate in turbulent times!
Join QuantUniversity for a complimentary summer speaker series where you will hear from Quants, innovators, startups and Fintech experts on various topics in Quant Investing, Machine Learning, Optimization, Fintech, AI etc.
Topic: Synthetic Data Generation in Finance
In this master class, Stefan shows how to create synthetic time-series data using generative adversarial networks (GAN). GANs train a generator and a discriminator network in a competitive setting so that the generator learns to produce samples that the discriminator cannot distinguish from a given class of training data. The goal is to yield a generative model capable of producing synthetic samples representative of this class. While most popular with image data, GANs have also been used to generate synthetic time-series data in the medical domain. Subsequent experiments with financial data explored whether GANs can produce alternative price trajectories useful for ML training or strategy backtests.
Reference:
1. 2019 NeurIPS Time-Series GAN paper by Jinsung Yoon, et al.
View all episodes
By Sri Krishnamurthy
5
11 ratings
Check out the upcoming speakers at: https://qufallschool.splashthat.com/
Subscribe to this podcast at www.anchor.fm/qupodcast
Or
On Apple Podcast at https://podcasts.apple.com/us/podcast/qupodcast/id1510865003
Slides and video at: https://academy.qusandbox.com/#/market/5fb54e3499aa4a24691da86c
A conversation with Quants, Thinkers and Innovators all challenged to innovate in turbulent times!
Join QuantUniversity for a complimentary summer speaker series where you will hear from Quants, innovators, startups and Fintech experts on various topics in Quant Investing, Machine Learning, Optimization, Fintech, AI etc.
Topic: Synthetic Data Generation in Finance
In this master class, Stefan shows how to create synthetic time-series data using generative adversarial networks (GAN). GANs train a generator and a discriminator network in a competitive setting so that the generator learns to produce samples that the discriminator cannot distinguish from a given class of training data. The goal is to yield a generative model capable of producing synthetic samples representative of this class. While most popular with image data, GANs have also been used to generate synthetic time-series data in the medical domain. Subsequent experiments with financial data explored whether GANs can produce alternative price trajectories useful for ML training or strategy backtests.
Reference:
1. 2019 NeurIPS Time-Series GAN paper by Jinsung Yoon, et al.