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--- |
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pipeline_tag: time-series-forecasting |
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tags: |
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- model_hub_mixin |
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- pytorch_model_hub_mixin |
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- time series foundation models |
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- pretrained models |
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- time series |
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--- |
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Moirai is a large pre-trained Time Series Model based on the Masked Encoder architecture. It is a universal time series forecasting model capable of addressing diverse forecasting tasks across multiple domains, frequencies, and variables in a zero-shot manner. |
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This is a version of [Moirai small](https://huggingface.co/Salesforce/moirai-1.1-R-small) trained by Faculty AI. It was pre-trained on the [LOTSA data](https://huggingface.co/datasets/Salesforce/lotsa_data) using the [codebase](https://github.com/SalesforceAIResearch/uni2ts/tree/main/cli/conf/pretrain) provided by Woo et al. (2024). Both the dataset and codebase are licensed under the [Apache License 2.0](https://www.apache.org/licenses/LICENSE-2.0). For more details on the model architecture, training, and results, please refer to the [paper](https://arxiv.org/abs/2402.02592). |
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### Usage |
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Please follow the [Installation](https://github.com/SalesforceAIResearch/uni2ts?tab=readme-ov-file#%EF%B8%8F-installation) instructions and [Getting Started](https://github.com/SalesforceAIResearch/uni2ts?tab=readme-ov-file#-getting-started) section provided in the uni2ts repo. To use the model trained by Faculty AI simply use `FacultyAI/moirai-small` when fetching the model weights. |
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``` |
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model = MoiraiForecast( |
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module=MoiraiModule.from_pretrained("FacultyAI/moirai-small"), |
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... |
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) |
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``` |
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References |
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```markdown |
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Woo, G., Liu, C., Kumar, A., Xiong, C., Savarese, S., & Sahoo, D. (2024). Unified Training of Universal Time Series Forecasting Transformers. arXiv preprint arXiv:2402.02592. |
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``` |
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