---
license: openmdw-1.1
library_name: simons
pipeline_tag: time-series-forecasting
datasets:
- abel-lab/Simons-Pretrain
thumbnail: https://huggingface.co/abel-lab/Simons-25M/resolve/main/assets/abel-color.png
tags:
- time-series
- forecasting
- probabilistic-forecasting
- foundation-model
- covariates
- pretrained
- pytorch
model-index:
- name: Simons-25M
results:
- task:
type: time-series-forecasting
dataset:
name: GIFT-Eval
type: Salesforce/GiftEval
metrics:
- name: MASE (relative to Seasonal Naive)
type: mase
value: 0.6860
- name: CRPS (relative to Seasonal Naive)
type: crps
value: 0.4683
source:
name: GIFT-Eval leaderboard
url: https://huggingface.co/spaces/Salesforce/GIFT-Eval
- task:
type: time-series-forecasting
dataset:
name: fev-bench
type: autogluon/fev_datasets
metrics:
- name: SQL skill score (%, relative to Seasonal Naive)
type: sql
value: 47.74
- name: MASE skill score (%, relative to Seasonal Naive)
type: mase
value: 36.27
source:
name: fev-bench leaderboard
url: https://huggingface.co/spaces/autogluon/fev-bench
- task:
type: time-series-forecasting
dataset:
name: TIME
type: Real-TSF/TIME
metrics:
- name: MASE (relative to Seasonal Naive)
type: mase
value: 0.6473
- name: CRPS (relative to Seasonal Naive)
type: crps
value: 0.5426
source:
name: TIME leaderboard
url: https://huggingface.co/spaces/Real-TSF/TIME-leaderboard
---
# Simons-25M
[](https://github.com/Abel-ai-lab/Simons)
[](https://huggingface.co/datasets/abel-lab/Simons-Pretrain)
Simons-25M is an open time series foundation model from Abel AI Lab for probabilistic forecasting.
It supports univariate and multivariate series with known-future covariates.
The [GitHub repository](https://github.com/Abel-ai-lab/Simons) contains inference and training code.
The release post, [Introducing Simons](https://abel.ai/research/introducing-simons/), tells how the models were built.
All four sizes and the training data are in the [Simons 1](https://huggingface.co/collections/abel-lab/simons-1-6ae3d4ce9705938e293c52dd) collection.
The [Simons Forecast Explorer](https://huggingface.co/spaces/abel-lab/simons-forecast-explorer) shows forecasts of the four sizes in the browser.
## Model details
- **Size:** 25.1M parameters.
- **Architecture:** a causal transformer over patches of 32 time steps, then an attention layer
across the variates of a series; 12 layers, width 384, 6 heads.
- **Context and horizon:** up to 16,384 time steps of history; 512 steps per forward pass, longer
horizons block by block.
- **Inputs:** up to 32 variates and up to 16 known-future covariates per series, numeric or
categorical; NaN marks missing values. Past-only covariates are accepted and dropped.
- **Outputs:** 9 quantiles (0.1, 0.2, ..., 0.9).
- **Training:** four stages, from synthetic pretraining to post-training on real data; the released
weights are the uniform average of the two continuations (Model soups).
- **Inference:** PyTorch on CPU or GPU, with the `simons` package, version 0.1.0 or later.
## Benchmarks
The place of Simons-297M and four widely used models on each benchmark. On
[GIFT-Eval](https://huggingface.co/spaces/Salesforce/GIFT-Eval), [fev-bench](https://huggingface.co/spaces/autogluon/fev-bench) and [TIME](https://huggingface.co/spaces/Real-TSF/TIME-leaderboard) the places are among the open-weight entries.
### Efficiency
Simons is fast to run: on one A100 GPU, Simons-297M forecasts a whole benchmark in minutes, about ten
times faster than models of similar size.
## Get started
```shell
pip install "git+https://github.com/Abel-ai-lab/Simons"
```
```python
import torch
from simons import Simons
device = "cuda" if torch.cuda.is_available() else "cpu"
model = Simons.from_pretrained("abel-lab/Simons-25M", device=device)
# [batch, time] or [batch, variates, time]; NaN marks missing values
history = torch.randn(8, 2000).cumsum(-1)
quantiles = model.forecast(history, horizon=96) # [8, 96, 9]
# Known-future covariates: [batch, covariates, time + horizon]
covariates = torch.randn(8, 2, 2000 + 96)
quantiles = model.forecast(history, horizon=96, covariates=covariates)
```
- **Training data:** [abel-lab/Simons-Pretrain](https://huggingface.co/datasets/abel-lab/Simons-Pretrain),
the public part of the data the models were trained on.
- **Getting started:** [docs/GETTING_STARTED.md](https://github.com/Abel-ai-lab/Simons/blob/main/docs/GETTING_STARTED.md) covers the input format,
covariates, forecast options and fine-tuning.
- **Models:** [docs/MODEL.md](https://github.com/Abel-ai-lab/Simons/blob/main/docs/MODEL.md) compares the four sizes and gives their scores.
- **Training:** [docs/TRAINING.md](https://github.com/Abel-ai-lab/Simons/blob/main/docs/TRAINING.md) covers the stages, configs and GPU settings.
- **Data:** [docs/DATA.md](https://github.com/Abel-ai-lab/Simons/blob/main/docs/DATA.md) describes what the models were trained on.
## Training data
- Synthetic series from the open generators in
[`src/simons/synth`](https://github.com/Abel-ai-lab/Simons/tree/main/src/simons/synth), with
synthetic known-future covariates of known effect.
- Public datasets with a permissive or share-alike license, most of them released as
[abel-lab/Simons-Pretrain](https://huggingface.co/datasets/abel-lab/Simons-Pretrain).
- Internal data: an in-house corpus.
See the
[dataset](https://huggingface.co/datasets/abel-lab/Simons-Pretrain) for data details
and the [training quickstart](https://github.com/Abel-ai-lab/Simons#train) for training commands.
Notices required by the training data's licenses: [NOTICE.md](NOTICE.md).
## License
The weights are licensed under OpenMDW-1.1 ([LICENSE](LICENSE)); the code on GitHub is licensed
under Apache-2.0 ([LICENSE](https://github.com/Abel-ai-lab/Simons/blob/main/LICENSE)). Each training
dataset keeps its own license; see [NOTICE.md](NOTICE.md) for details.
## 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}
}
```