moirai-1.0-R-large
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moirai-1.0-R-small
SalesforceMoirai, the Masked Encoder-based Universal Time Series Forecasting Transformer is a Large Time Series Model pre-trained on LOTSA data. For more details on the Moirai architecture, training, and results, please refer to the paper. Fig. 1: Overall architecture of Moirai. Visualized is a 3-variate time series, where variates 0 and 1 are target variables (i.e. to be forecasted, and variate 2 is a dynamic covariate (values in forecast horizon known). Based on a patch size of 64, each variate is patchified into 3 tokens. The patch embeddings along with sequence and variate id are fed into the Transformer. The shaded patches represent the forecast horizon to be forecasted, whose corresponding output representations are mapped into the mixture distribution parameters. This release is for research purposes only in support of an academic paper. Our models, datasets, and code are not specifically designed or evaluated for all downstream purposes. We strongly recommend users evaluate and address potential concerns related to accuracy, safety, and fairness before deploying this model. We encourage users to consider the common limitations of AI, comply with applicable laws, and leverage best practices when selecting use cases, particularly for high-risk scenarios where errors or misuse could significantly impact people’s lives, rights, or safety. For further guidance on use cases, refer to our AUP and AI AUP.
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詳細を見る ↗moirai-1.1-R-base
SalesforceThis is new updated version of Moirai-1.0-R (https://huggingface.co/Salesforce/moirai-1.0-R-base). The new Moirai model achieved significant improvements (~20%) for low-frequency cases like Yearly and Quarterly data in Normalised Mean Absolute Error (NMAE) for 40 datasets on the Monash repository. This release is for research purposes only in support of an academic paper. Our models, datasets, and code are not specifically designed or evaluated for all downstream purposes. We strongly recommend users evaluate and address potential concerns related to accuracy, safety, and fairness before deploying this model. We encourage users to consider the common limitations of AI, comply with applicable laws, and leverage best practices when selecting use cases, particularly for high-risk scenarios where errors or misuse could significantly impact people’s lives, rights, or safety. For further guidance on use cases, refer to our AUP and AI AUP.
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詳細を見る ↗moirai-1.1-R-large
SalesforceThis is new updated version of Moirai-1.0-R (https://huggingface.co/Salesforce/moirai-1.0-R-large). The Moirai-1.1-R model achieved significant improvements (~20%) for low-frequency cases like Yearly and Quarterly data in Normalised Mean Absolute Error (NMAE) for 40 datasets on the Monash repository. This release is for research purposes only in support of an academic paper. Our models, datasets, and code are not specifically designed or evaluated for all downstream purposes. We strongly recommend users evaluate and address potential concerns related to accuracy, safety, and fairness before deploying this model. We encourage users to consider the common limitations of AI, comply with applicable laws, and leverage best practices when selecting use cases, particularly for high-risk scenarios where errors or misuse could significantly impact people’s lives, rights, or safety. For further guidance on use cases, refer to our AUP and AI AUP.
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詳細を見る ↗moirai-1.1-R-small
SalesforceThis is new updated version of Moirai-1.0-R (https://huggingface.co/Salesforce/moirai-1.0-R-base). The Moirai-1.1-R model achieved significant improvements (~20%) for low-frequency cases like Yearly and Quarterly data in Normalised Mean Absolute Error (NMAE) for 40 datasets on the Monash repository. This release is for research purposes only in support of an academic paper. Our models, datasets, and code are not specifically designed or evaluated for all downstream purposes. We strongly recommend users evaluate and address potential concerns related to accuracy, safety, and fairness before deploying this model. We encourage users to consider the common limitations of AI, comply with applicable laws, and leverage best practices when selecting use cases, particularly for high-risk scenarios where errors or misuse could significantly impact people’s lives, rights, or safety. For further guidance on use cases, refer to our AUP and AI AUP.
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詳細を見る ↗moirai-2.0-R-small
SalesforceMoirai 2.0 is a decoder-only universal time series forecasting transformer model pre-trained on: - Subset of GIFT-Eval Pretrain, and Train datasets (Non-leaking historical context). - Mixup data generated from non-leaking subsets of Chronos Dataset. - Synthetic time series produced via KernelSynth introduced in Chronos paper. - Internal Salesforce operational data. We make significant improvements over the first version of Moirai (please refer to the paper for previous version): - Switched from a distributional loss to a quantile loss formulation. - Moved from single-token to multi-token prediction, improving efficiency and stability. - Added a data filtering mechanism to filter out non-forecastable, low quality, time series during pretraining. - Added a new patch token embedding which includes missing value information. - Added patch-level random mask to improve robustness of the model during inference. To perform inference with Moirai 2.0, install the uni2ts library from our GitHub repo.
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詳細を見る ↗moirai-agent
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詳細を見る ↗moirai-moe-1.0-R-base
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詳細を見る ↗moirai-moe-1.0-R-small
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詳細を見る ↗FlexTab
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詳細を見る ↗基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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詳細を見る ↗LimiX-2
stable-ai基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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詳細を見る ↗Nori
Synthefy**Nori** is a tabular foundation model for **regression** via in-context learning (ICL). Given a few labeled rows as context, it predicts on new query rows in a **single forward pass**, with no task-specific training or fine-tuning. The model is trained **entirely on synthetic data**. - **Documentation:** https://docs.synthefy.com/nori/ - **Repository:** https://github.com/Synthefy/synthefy-nori - **Library:** `pip install synthefy-nori` - **Checkpoint:** `nori.pt` (this repo) - **Parameters:** ~5.9M - **License:** Apache-2.0 Mean and median R² of the base model across 96 regression tasks from three public benchmark suites (single H200, up to 50K context rows per dataset):
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詳細を見る ↗Nori-1-5
Synthefy基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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詳細を見る ↗Nori-100M
Synthefy**Nori-100M** is the ~98.3M-parameter variant of Nori, a tabular foundation model for **regression** via in-context learning (ICL). Given a few labeled rows as context, it predicts on new query rows in a **single forward pass**, with no task-specific training or fine-tuning. The model is trained **entirely on synthetic data**. - **Documentation:** https://docs.synthefy.com/nori/ - **Repository:** https://github.com/Synthefy/synthefy-nori - **Library:** `pip install synthefy-nori` - **Checkpoint:** `nori.pt` (this repo) - **Parameters:** ~98.3M (98,304,698) - **Architecture:** 42 layers, embed_dim 352, hid_dim 1056, 8 heads - **License:** Apache-2.0 It uses a GPU when one is available and falls back to CPU. A one-shot helper skips the object entirely:
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詳細を見る ↗Nori-30M
Synthefy**Nori-30M** is the ~29.2M-parameter variant of Nori, a tabular foundation model for **regression** via in-context learning (ICL). Given a few labeled rows as context, it predicts on new query rows in a **single forward pass**, with no task-specific training or fine-tuning. The model is trained **entirely on synthetic data**. - **Documentation:** https://docs.synthefy.com/nori/ - **Repository:** https://github.com/Synthefy/synthefy-nori - **Library:** `pip install synthefy-nori` - **Checkpoint:** `nori.pt` (this repo) - **Parameters:** ~29.2M - **License:** Apache-2.0 Mean and median R² across 96 regression tasks from three public benchmark suites, on the same protocol as the base Nori:
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詳細を見る ↗tafsut-univariate-base
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詳細を見る ↗t0-alpha
theforecastingcompany`t0-alpha` is an open-weights time-series forecasting foundation model from The Forecasting Company. `t0` is a transformer-based model that produces probabilistic multi-horizon forecasts and natively operates on multiple covariates. `t0-alpha` is the first public iteration of the model. You can use `t0` on Retrocast, The Forecasting Company's platform for forecasting on your own data and comparing forecasts across open-weight models.
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詳細を見る ↗t0-beta
theforecastingcompany`t0-beta` is an open-weights time-series forecasting foundation model from The Forecasting Company. `t0` is a transformer-based model that produces probabilistic multi-horizon forecasts and natively operates on multiple covariates. `t0-beta` is the second public iteration of the model. It predicts 21 quantile levels natively, and is an incremental improvement over `t0-alpha`.
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詳細を見る ↗t0-gamma
theforecastingcompany`t0-gamma` is an open-weights time-series forecasting foundation model from The Forecasting Company. `t0` is a transformer-based model that produces probabilistic multi-horizon forecasts and natively operates on multiple covariates. `t0-gamma` is the third public iteration of the model. You can use `t0` on Retrocast, The Forecasting Company's platform for forecasting on your own data and comparing forecasts across open-weight models.
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詳細を見る ↗sundial-base-128m
thuml🚩 **News (2025.08)** Sundial has been integrated into Apache IoTDB, a native time-series database. 🚩 **News (2025.05)** Get **1st MASE** on the GIFT-Eval Benchmark. 🚩 **News (2025.02)** Get **1st MSE/MAE** zero-shot performance on Time-Series-Library datasets.
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詳細を見る ↗timer-base-84m
thuml基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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詳細を見る ↗TimeBraid-2.5B
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詳細を見る ↗FoMo-0D
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