paris-noah/CauKer2M
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. 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… See the full description on the dataset page: https://huggingface.co/datasets/paris-noah/CauKer2M.
| Author / organization | paris-noah |
|---|---|
| License (as declared) | apache-2.0 |
| Downloads | 173 (as reported by Hugging Face) |
| Likes | 3 |
| Created | 28 November 2025 |
| Last updated at source | 13 March 2026 |
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- 17 Oct 2026 Description: 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 Se → 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 Se
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