| --- |
| license: openmdw-1.1 |
| license_name: per-dataset |
| license_link: https://huggingface.co/datasets/abel-lab/Simons-Pretrain/blob/main/NOTICE.md |
| task_categories: |
| - time-series-forecasting |
| tags: |
| - time-series |
| - forecasting |
| - pretraining |
| pretty_name: Simons Pretraining Data |
| size_categories: |
| - 10B<n<100B |
| configs: |
| - config_name: univariate |
| data_files: univariate/*/*.parquet |
| default: true |
| - config_name: covariates |
| data_files: covariates/*/*.parquet |
| --- |
| |
| # Simons Pretraining Data |
|
|
| The real-world training data of the [Simons](https://huggingface.co/collections/abel-lab/simons-1-6ae3d4ce9705938e293c52dd) time series |
| foundation models. |
| The synthetic training data is generated by the training code. |
|
|
| [💻 Code](https://github.com/Abel-ai-lab/Simons) · [🤗 Models](https://huggingface.co/collections/abel-lab/simons-1-6ae3d4ce9705938e293c52dd) · |
| [📄 Training data overview](https://github.com/Abel-ai-lab/Simons/blob/main/docs/DATA.md) · |
| [📝 Release post](https://abel.ai/research/introducing-simons/) |
|
|
| ## Statistics |
|
|
| <table> |
| <tr><td><b>Observations</b></td><td>11.3B</td></tr> |
| <tr><td><b>Datasets</b></td><td>62 (univariate), 59 (covariate)</td></tr> |
| <tr><td><b>Series</b></td><td>7.5M (univariate), 6k (covariate)</td></tr> |
| <tr><td><b>Domains</b></td><td>11</td></tr> |
| <tr><td><b>Sampling frequencies</b></td><td>Sub-second to monthly</td></tr> |
| </table> |
|
|
| The models train on this data together with synthetic series. |
|
|
|
|
| ## Usage |
|
|
| With the [training code](https://github.com/Abel-ai-lab/Simons), which downloads the data on first use |
| and trains one size end to end, as the released models were trained: |
|
|
| ```bash |
| scripts/train.sh 25m abel-lab/Simons-Pretrain abel-lab/Simons-Pretrain # 25m, 60m, 178m, 297m |
| ``` |
|
|
| With 🤗 Datasets: |
|
|
| ```python |
| from datasets import load_dataset |
| |
| univariate = load_dataset("abel-lab/Simons-Pretrain", "univariate", split="train") |
| covariates = load_dataset("abel-lab/Simons-Pretrain", "covariates", split="train") |
| ``` |
|
|
| ## Layout |
|
|
| One folder per dataset: |
|
|
| - `univariate/<dataset>/train-*.parquet`: `item_id`, `source`, `freq`, `domain`, `subdomain`, |
| `target` (float32 `[variate][time]`, NaN = missing). Long series are cut into segments |
| `<series>@<offset>`, which the long-context stages stitch back together. |
| - `covariates/<dataset>/train-*.parquet`: `item_id`, `source`, `freq`, `target`, `covariates` |
| (`[covariate][time]`, aligned with `target`), `covariate_names`. |
|
|
| `manifest.json` lists every dataset with its source, frequency and license. |
|
|
| Series were cleaned (invalid values masked, duplicates removed) and given neutral ids. |
|
|
| ## Licences |
|
|
| Each dataset retains its original license. [NOTICE.md](NOTICE.md) lists each dataset's source, license, and required attributions, with links to the license texts. |
|
|
| ## Citation |
|
|
| ```bibtex |
| @misc{simons2026, |
| title = {Simons: Building Open Time Series Foundation Models through Auto-Research}, |
| author = {{Abel AI Lab}}, |
| year = {2026}, |
| url = {https://github.com/Abel-ai-lab/Simons} |
| } |
| ``` |
