GenTS: A Comprehensive Benchmark Library for Generative Time Series Models

GenTS is an open-source library for researchers to conduct generative time series analysis, which covers (class label-guided) synthesis, forecasting and imputation.

✨ Highlights

Based on lightning, GenTS provides a modular code base to benchmark different models on various datasets in a easy way. Specifically, we feature on:

  • State-of-the-art Models: Generative time series models from top conferences in 5 recent years, including Diffusions, Flows, GANs, etc.

  • Multi-domain Datasets: Various time series datasets from energy, health, and other domains.

  • Comprehensive Evaluation: Both quantitative and qualitative methods for fidelity, usefulness, and accuracy.

⚙️ Installation

We recommand to first create a virtual environment, and activate the environment. Then you can install the necessary libraries by running the following command.

conda create -n gents python=3.10
conda activate gents
pip install -r requirements.txt
pip install -e .

🚖 Quick start

A minimal example of (unconditional) time series generation:

import torch
from gents.model import VanillaDDPM
from gents.dataset import SineND
from gents.evaluation import tsne_visual
from lightning import Trainer

# setup dataset and model
dm = SineND(seq_len=64, seq_dim=2, batch_size=64)
model = VanillaDDPM(seq_len=dm.seq_len, seq_dim=dm.seq_dim)

# training (on CPU for example)
trainer = Trainer(max_epochs=100, accelerator="cpu")
trainer.fit(model, dm)

# testing
dm.setup("test")
real_data = torch.cat([batch["seq"] for batch in dm.test_dataloader()])  # [N, 64, 2]
gen_data = model.sample(n_sample=len(real_data))  # [N, 64, 2]

# visualization with tsne
tsne_visual(real_data, gen_data, save_root="tsne.png")

We also provide tutorials for different use case of generative time series models, including common conditional generation tasks (forecasting, imputation, class label-guided synthesis) and others. Please refer to tutorials/ or our documents for detailed examples.

🔧 Custormization

Our BaseModel and datamodules BaseData are based on lightning,

One can easily join GenTS following the lightning.LightningModule and lightning.DataModule. Please refer to our document’s tutorial on custormization for details, or this website.

🙏 All Thanks To Our Contributors

📎 Citation

If you enjoy or benefit from using GenTS, a citation to this repository will be greatly appreciated！

@misc{wang2025a,
title={GenTS: A Comprehensive Benchmark Library for Generative Time Series Models},
author={Chenxi Wang, Xiaorong Wang, Peiyang Li, Yi Wang},
year={2025},
url={https://github.com/WillWang1113/GenTS}
}