PaD-TS
September 1, 2025 ยท View on GitHub
The repo is the official implementation for the paper: Population Aware Diffusion for Time Series Generation.
Population-aware Diffusion for Time Series (PaD-TS) is a new TS generation model that better preserves the population-level properties. The key novelties of PaD-TS include 1) a new training method explicitly incorporating TS population-level property preservation, and 2) a new dual-channel encoder model architecture that better captures the TS data structure.
Training and Architecture
PaD-TS training
Dual-Channel Model Architecture
Setup & Experiments
Environment setup.
$ conda env create --name PaD-TS --file=PaD-TS.yml
$ conda activate PaD-TS
Running experiment
$ python run.py -d {name} >& results/{name}.txt
Results
TS generation results with generation length 24 for Sines, Stocks, and Energy datasets. Bold font (lower score) indicates the best performance.
Long TS Generation Results on Energy dataset. Bold font (lower score) indicates the best performance.
Citation
If you find this repo useful, please cite our paper!
@article{Li_Meng_Bi_Urnes_Chen_2025,
title={Population Aware Diffusion for Time Series Generation},
volume={39},
url={https://ojs.aaai.org/index.php/AAAI/article/view/34038},
DOI={10.1609/aaai.v39i17.34038},
number={17},
journal={Proceedings of the AAAI Conference on Artificial Intelligence},
author={Li, Yang and Meng, Han and Bi, Zhenyu and Urnes, Ingolv T. and Chen, Haipeng},
year={2025},
month={Apr.},
pages={18520-18529}
}
Code
Thanks for the open sources papers listed below which PaD-TS is build on.
https://github.com/openai/improved-diffusion/tree/main
https://github.com/thuml/iTransformer
https://github.com/facebookresearch/DiT