paris-noah/CauKer2M

--- 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} } ```