paris-noah/CauKer2M
收藏Hugging Face2026-03-13 更新2025-12-20 收录
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https://hf-mirror.com/datasets/paris-noah/CauKer2M
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资源简介：
---
license: apache-2.0
task_categories:
- other
tags:
- time-series
- synthetic-data
- foundation-models
---
# CauKer: Classification Time Series Foundation Models Can Be Pretrained on Synthetic Data Only
This repository contains synthetic time series data generated using the **CauKer** framework, as presented in the paper [CauKer: Classification Time Series Foundation Models Can Be Pretrained on Synthetic Data](https://huggingface.co/papers/2508.02879).
CauKer is a synthetic data generation framework for pre-training classification Time Series Foundation Models (TSFMs) without relying on real-world data. It combines Gaussian Process (GP) kernel composition with Structural Causal Models (SCM) to produce diverse and causally coherent sequences with realistic trends and seasonality.
- **Paper:** [CauKer: Classification Time Series Foundation Models Can Be Pretrained on Synthetic Data](https://huggingface.co/papers/2508.02879)
- **GitHub Repository:** [https://github.com/ShifengXIE/CauKer](https://github.com/ShifengXIE/CauKer)
## Generation Script Usage
If you wish to generate your own synthetic data using the CauKer framework, you can use the following command from the official repository:
```bash
# Generate 200,000 synthetic time series (default: 512-length, 4-dimensional)
python CauKer.py -N 200000 -L 512 -F 4 -P 6 -M 18 -O CauKer200K.arrow
```
## Citation
If you find this dataset or the CauKer framework useful, please cite:
```bibtex
@inproceedings{cauker2025,
title={CauKer: Classification Time Series Foundation Models Can Be Pretrained on Synthetic Data Only},
author={Shifeng Xie, Vasilii Feofanov, Marius Alonso, Ambroise Odonnat, Jianfeng Zhang, Ievgen Redko},
booktitle={ICML Workshop on Foundation Models for Structured Data (FMSD)},
year={2025}
}
```
提供机构：
paris-noah
