JonathanCrabbe/FourierDiffusion
This repository implements time series diffusion in the frequency domain.
This tool helps researchers and data scientists generate realistic synthetic time series data by training diffusion models in the frequency domain. You input existing time series datasets (like ECG or financial data), and it outputs new, synthetic time series samples that mimic the characteristics of your original data. It's designed for anyone working with time series data who needs to augment datasets or understand underlying data generation processes.
No commits in the last 6 months.
Use this if you need to generate high-quality synthetic time series data for tasks like anomaly detection, forecasting, or privacy-preserving data sharing.
Not ideal if you're looking for a simple, off-the-shelf solution for basic time series analysis without model training.
Stars
61
Forks
10
Language
Jupyter Notebook
License
MIT
Category
Last pushed
Mar 02, 2025
Commits (30d)
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