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This solution accelerator demonstrates how to use various generative AI forecasting models from within Databricks.
Ryuta Yoshimatsu [email protected]
Puneet Jain [email protected]
Please note the code in this project is provided for your exploration only, and are not formally supported by Databricks with Service Level Agreements (SLAs). They are provided AS-IS and we do not make any guarantees of any kind. Please do not submit a support ticket relating to any issues arising from the use of these projects. The source in this project is provided subject to the Databricks License. All included or referenced third party libraries are subject to the licenses set forth below.
Any issues discovered through the use of this project should be filed as GitHub Issues on the Repo. They will be reviewed as time permits, but there are no formal SLAs for support.
© 2024 Databricks, Inc. All rights reserved. The source in this notebook is provided subject to the Databricks License [https://databricks.com/db-license-source]. All included or referenced third party libraries are subject to the licenses set forth below.
| library | description | license | source |
|---|---|---|---|
| datasetsforecast | Datasets for Time series forecasting | MIT | https://pypi.org/project/datasetsforecast/ |
| chronos | A family of pretrained time series forecasting models based on language model architectures | Apache 2.0 | https://github.com/amazon-science/chronos-forecasting |
| moirai | The Masked Encoder-based Universal Time Series Forecasting Transformer, a Large Time Series Model pre-trained on LOTSA data | Apache 2.0 | https://huggingface.co/collections/Salesforce/moirai-10-r-models-65c8d3a94c51428c300e0742 |
| moment | A family of open-source foundation models for general-purpose time-series analysis | MIT License | https://github.com/moment-timeseries-foundation-model/moment |
| timegpt | A production ready, generative pretrained transformer for time series | Apache | https://github.com/Nixtla/nixtla |
| timesfm | A pretrained time-series foundation model developed by Google Research for time-series forecasting | Apache 2.0 | https://github.com/google-research/timesfm |