Datasets:

abel-lab
/

Simons-Pretrain

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 time series foundation models. The synthetic training data is generated by the training code.

💻 Code · 🤗 Models · 📄 Training data overview · 📝 Release post

Statistics

Observations	11.3B
Datasets	62 (univariate), 59 (covariate)
Series	7.5M (univariate), 6k (covariate)
Domains	11
Sampling frequencies	Sub-second to monthly

The models train on this data together with synthetic series.

Usage

With the training code, which downloads the data on first use and trains one size end to end, as the released models were trained:

scripts/train.sh 25m abel-lab/Simons-Pretrain abel-lab/Simons-Pretrain   # 25m, 60m, 178m, 297m

With 🤗 Datasets:

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 lists each dataset's source, license, and required attributions, with links to the license texts.

Citation

@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}
}