Simons-178M

Co-authored-by: RuiLGTM <RuiLGTM@users.noreply.huggingface.co>
Co-authored-by: jiyuantan <jiyuantan@users.noreply.huggingface.co>

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LICENSE

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+Copyright (c) 2026 Abel AI
+
+OpenMDW License Agreement, version 1.1 (OpenMDW-1.1)
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NOTICE.md

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+# Notices
+
+Simons, Copyright 2026 Abel AI Lab (https://abel.ai).
+
+## Notices required by the training data's licenses
+
+Each training dataset of the Simons models keeps its own license and terms; the data were cleaned,
+resampled and cut into training windows.
+
+- ERA5: Contains modified Copernicus Climate Change Service information 2026. Neither the European
+  Commission nor ECMWF is responsible for any use that may be made of the Copernicus information
+  or data it contains.
+- CAMS European air-quality forecasts: Contains modified Copernicus Atmosphere Monitoring Service
+  information 2026. Neither the European Commission nor ECMWF is responsible for any use that may
+  be made of the Copernicus information or data it contains.
+- ECMWF IFS weather forecasts, retrieved through the Open-Meteo archive: Weather data by
+  Open-Meteo.com (https://open-meteo.com/), CC BY 4.0. Based on data and products of the European
+  Centre for Medium-Range Weather Forecasts (ECMWF, https://www.ecmwf.int/), CC BY 4.0.
+- NASA POWER: Weather data were obtained from the NASA Langley Research Center POWER Project
+  funded through the NASA Earth Science Directorate Applied Science Program.
+- NCEP/CPC L3 Half Hourly 4km Global Merged IR (GPM_MERGIR): NASA is the source of these data
+  (https://disc.gsfc.nasa.gov/datasets/GPM_MERGIR_1/summary).
+- Elexon BMRS: Contains BMRS data © Elexon Limited copyright and database right 2026.
+- National Highways WebTRIS: Contains public sector information licensed under the Open Government
+  Licence v3.0.
+- NESO Data Portal: Supported by National Energy SO Open Data.
+- EirGrid: Supported by EirGrid Group Data.
+- Retrosheet: The information used here was obtained free of charge from and is copyrighted by
+  Retrosheet. Interested parties may contact Retrosheet at "www.retrosheet.org".
+- WorldTrace: Contains information from
+  [WorldTrace (UniTraj)](https://huggingface.co/datasets/OpenTrace/WorldTrace), which is made
+  available under the
+  [Open Database License (ODbL) 1.0](https://opendatacommons.org/licenses/odbl/1-0/). Map
+  matching uses data © [OpenStreetMap contributors](https://www.openstreetmap.org/copyright). The
+  WorldTrace-derived training data are offered under the ODbL 1.0 in
+  [Simons-Pretrain](https://huggingface.co/datasets/abel-lab/Simons-Pretrain)
+  (`univariate/worldtrace/`).
+- Banco Central do Brasil Focus survey: Contains information from the
+  [Expectativas de Mercado dataset](https://dadosabertos.bcb.gov.br/dataset/expectativas-mercado)
+  (Focus survey) of Banco Central do Brasil, which is made available under the
+  [Open Database License (ODbL) 1.0](https://opendatacommons.org/licenses/odbl/1-0/). Fonte: Banco
+  Central do Brasil, Sistema de Expectativas de Mercado. The derived data are offered under the ODbL
+  1.0 in [Simons-Pretrain](https://huggingface.co/datasets/abel-lab/Simons-Pretrain)
+  (`covariates/bcb_focus_inflation/`).
+- opentransportdata.swiss: Source of the raw data: opentransportdata.swiss.
+- RTE eCO2mix: Data from RTE éCO2mix (https://odre.opendatasoft.com/), Licence Ouverte 2.0.
+- CausalRivers: Data from CausalRivers (https://causalrivers.github.io/), licensed under
+  dl-de/by-2-0 (https://www.govdata.de/dl-de/by-2-0).
+- AEMO: Contains data from the Australian Energy Market Operator (AEMO), used under AEMO's
+  copyright permissions.
+- Elia: Contains data from Elia Open Data (https://opendata.elia.be/).
+- LargeAQ: from CauAir (https://github.com/PoorOtterBob/CauAir), CC BY 4.0. Ma et al., "Causal
+  Learning Meet Covariates: Empowering Lightweight and Effective Nationwide Air Quality
+  Forecasting", IJCAI 2025 (https://www.ijcai.org/proceedings/2025/353).
+- CC BY-SA 4.0: training data from
+  [Building Data Genome Project 2](https://github.com/buds-lab/building-data-genome-project-2),
+  [Open D1NAMO](https://zenodo.org/records/5651217),
+  [NMT Scalp EEG Dataset](https://dll.seecs.nust.edu.pk/downloads/)
+  ([Khan et al. 2022](https://doi.org/10.3389/fnins.2021.755817)), the
+  [HAI industrial control system dataset](https://github.com/icsdataset/hai) and
+  [Wikipedia](https://en.wikipedia.org/) club-season, episode-list and team-season articles
+  (Wikipedia contributors; the team-season articles give the NBA, NHL and WNBA event dates in
+  `nyc_subway_events`), licensed under
+  [CC BY-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0/); the redistributed copies are in
+  [Simons-Pretrain](https://huggingface.co/datasets/abel-lab/Simons-Pretrain).
+- The datasets in Simons-Pretrain are credited, with their licenses and links to their
+  sources, in its
+  [NOTICE.md](https://huggingface.co/datasets/abel-lab/Simons-Pretrain/blob/main/NOTICE.md).

README.md

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+---
+license: openmdw-1.1
+library_name: simons
+pipeline_tag: time-series-forecasting
+datasets:
+- abel-lab/Simons-Pretrain
+thumbnail: https://huggingface.co/abel-lab/Simons-178M/resolve/main/assets/abel-color.png
+tags:
+- time-series
+- forecasting
+- probabilistic-forecasting
+- foundation-model
+- covariates
+- pretrained
+- pytorch
+model-index:
+- name: Simons-178M
+  results:
+  - task:
+      type: time-series-forecasting
+    dataset:
+      name: GIFT-Eval
+      type: Salesforce/GiftEval
+    metrics:
+    - name: MASE (relative to Seasonal Naive)
+      type: mase
+      value: 0.6754
+    - name: CRPS (relative to Seasonal Naive)
+      type: crps
+      value: 0.4609
+    source:
+      name: GIFT-Eval (scored with the benchmark code)
+      url: https://github.com/SalesforceAIResearch/gift-eval
+---
+
+<p align="center">
+  <img class="dark:hidden"
+src="https://huggingface.co/abel-lab/Simons-178M/resolve/main/assets/abel-color.png" alt="Abel"
+width="240" />
+  <img class="hidden dark:block"
+src="https://huggingface.co/abel-lab/Simons-178M/resolve/main/assets/abel-white.png" alt="Abel"
+width="240" />
+</p>
+
+# Simons-178M
+
+Simons-178M 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.
+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:** 177.7M parameters.
+- **Architecture:** a causal transformer over patches of 32 time steps, then an attention layer
+  across the variates of a series; 12 layers, width 1024, 16 heads.
+- **Context and horizon:** up to 32,768 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
+
+<p align="center">
+  <img class="dark:hidden"
+src="https://huggingface.co/abel-lab/Simons-178M/resolve/main/assets/benchmark-places.png" alt="Places on public benchmark leaderboards"
+width="720" />
+  <img class="hidden dark:block"
+src="https://huggingface.co/abel-lab/Simons-178M/resolve/main/assets/benchmark-places-dark.png" alt="Places on public benchmark leaderboards"
+width="720" />
+</p>
+
+The place of Simons-297M and four widely used models on each benchmark's own leaderboard. On
+GIFT-Eval, fev-bench and TIME the places are among the open-weight entries, from the snapshots used
+for this release (GIFT-Eval: October 30, 2026).
+
+### 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.
+
+<p align="center">
+  <img class="dark:hidden"
+src="https://huggingface.co/abel-lab/Simons-178M/resolve/main/assets/inference-time.png" alt="Time to forecast GIFT-Eval, TIME and fev-bench"
+width="720" />
+  <img class="hidden dark:block"
+src="https://huggingface.co/abel-lab/Simons-178M/resolve/main/assets/inference-time-dark.png" alt="Time to forecast GIFT-Eval, TIME and fev-bench"
+width="720" />
+</p>
+
+## 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-178M", 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}
+}
+```

config.json

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+{
+  "model_type": "simons",
+  "context_length": 32768,
+  "prediction_length": 512,
+  "patch_length": 32,
+  "width": 1024,
+  "layers": 12,
+  "heads": 16,
+  "near_context": 4096,
+  "far_pool": 8,
+  "far_min_history": 7680,
+  "stats_window": 8192,
+  "period_slots": 4,
+  "period_alpha": 0.05,
+  "period_local": 5.0,
+  "detector_mid": 7680,
+  "views": true,
+  "tail_paths": [
+    1,
+    4,
+    7
+  ],
+  "sign_clip": true,
+  "integer_snap": true,
+  "covariates": true,
+  "binary_rows": true,
+  "fit_row": true,
+  "past_rows": false,
+  "read_after": 5,
+  "read_binary": true,
+  "xreg": true,
+  "xreg_gate": 0.05,
+  "xreg_square": false,
+  "xreg_guard": true,
+  "xreg_guard_margin": 0.9,
+  "xreg_guard_min": 0,
+  "rows_only_history": 4096
+}

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