Financial Data Generation

Project: Time Series Generation of Financial Data

Language: Python

Tasks:

1. Implement Losses: Developing and applying loss functions suitable for financial time series data.
2. Implement Generative Models: Utilizing Variational Autoencoders (VAE) and Generative Adversarial Networks (GAN) to generate synthetic financial data.
3. LU-factorization: Implementing and applying LU decomposition for matrix factorization in the context of financial data.
4. Gaussian Model: Developing and refining Gaussian models for noise and data generation.

Files and Notebooks:

• Various Jupyter notebooks (e.g., gan_v1.ipynb, vae.ipynb) for developing and testing generative models.
• Python scripts (e.g., main.py, train.py) for implementing core functionalities.
• Configuration files (e.g., requirements.txt, env.yml) for setting up the environment and dependencies.

Language: Python Task: Implement Losses, Implement Generative Models, LU-factorization, Gaussian Model

Code : financial-data-generation