ML4ITS/synthetic-data
Generate synthetic time-series using generative adversarial networks. Functional end-to-end system for dataset generation, model registry/inferences and UI interface for evaluation.
This project helps operations engineers, data scientists, and researchers create realistic synthetic time-series data without exposing or using sensitive real-world information. You provide historical time-series data, and it generates new, artificial time-series that mimic the original's patterns and characteristics. This is useful for testing new algorithms or models when real data is scarce or privacy-restricted.
No commits in the last 6 months.
Use this if you need to generate high-quality, synthetic time-series datasets for development, testing, or research purposes without relying on or exposing actual historical data.
Not ideal if you require explainable AI or direct causal inference from your synthetic data, as generative models are primarily focused on pattern replication.
Stars
19
Forks
5
Language
Jupyter Notebook
License
—
Category
Last pushed
Aug 19, 2022
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/generative-ai/ML4ITS/synthetic-data"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
sdv-dev/SDV
Synthetic data generation for tabular data
sdv-dev/SDGym
Benchmarking synthetic data generation methods.
NVIDIA-NeMo/DataDesigner
🎨 NeMo Data Designer: A general library for generating high-quality synthetic data from scratch...
AlexanderVNikitin/tsgm
Generation and evaluation of synthetic time series datasets (also, augmentations,...
mostly-ai/mostlyai
Synthetic Data SDK ✨