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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Deep learning PyTorch library for time series forecasting, classification, and anomaly detection (originally for flood forecasting).
| Date | Stars |
|---|---|
| 2026-07-24 | 2288 |
| 2026-07-25 | 2288 |
| 2026-07-28 | 2288 |
| 2026-07-30 | 2288 |
| 2026-07-31 | 2290 |
| 2026-08-06 | 2290 |
Today
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This week
+2 stars this week
This month
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Momentum
2.0
growth rate 0.09%/day
# Deep learning for time series forecasting, classification, and anomaly detection  Flow Forecast (FF) is an open-source deep learning for time series forecasting framework. It provides all the latest state of the art models (transformers, attention models, GRUs, ODEs) and cutting edge concepts with easy to understand interpretability metrics, cloud provider integration, and model serving capabilities. Flow Forecast was the first time series framework to feature support for transformer based models and remains the only true end-to-end deep learning for time series framework. Currently, [Task-TS from CoronaWhy](https://github.com/CoronaWhy/task-ts/wiki) primarily maintains this repository. Pull requests are welcome. Historically, this repository provided open source benchmark and codes for flash flood and river flow forecasting. For additional tutorials and examples please see our [tutorials repository](https://github.com/AIStream-Peelout/flow_tutorials). | branch | status $$ | | --- | --- | | master | [](https://dl.circleci.com/status-badge/redirect/gh/AIStream-Peelout/flow-forecast/tree/master) | | | Build PY| | | Documentation | [](https://flow-forecast.readthedocs.io/en/latest/)| | CodeCov| [](https://codecov.io/gh/AIStream-Peelout/flow-forecast)| | CodeFactor| [](https://www.codefactor.io/repository/github/aistream-peelout/flow-forecast)| ## Getting Started Using the library 1. Run `pip install flood-forecast` 2. Detailed info on training models can be found on the [Wiki](https://flow-forecast.atlassian.net/wiki/spaces/FF/pages/364019713/Training+Models). 3. Check out our [Confluence Documentation](https://flow-forecast.atlassian.net/wiki/spaces/FF/overview) **Models currently supported** 1. Vanilla LSTM (LSTM): A basic LSTM that is suitable for multivariate time series forecasting and transfer learning. 2. Full transformer (SimpleTransformer in model_dict): The full original transformer with all 8 encoder and decoder blocks. Requires passing the target in at inference. 3. Simple Multi-Head Attention (MultiHeadSimple): A simple multi-head attention block and linear embedding layers. Suitable for transfer learning. 4. Transformer with a linear decoder (CustomTransformerDecoder in model_dict): A transformer with n-encoder blocks (this is tunable) and a linear decoder. Suitable for forecasting, classification or anomaly detection. 5. [DA-RNN](https://arxiv.org/abs/1704.02971): (DARNN) A well rounded model with which utilizes a LSTM + attention. 6. [Enhancing the Locality and Breaking the Memory Bottleneck of Transformer on Time Series Forecasting](https://arxiv.org/abs/1907.00235) (called DecoderTransformer in model_dict): 7. [Transformer XL](https://arxiv.org/abs/1901.02860): Porting Transformer XL for time series. 8. [Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting](https://arxiv.org/abs/2012.07436) (Informer) 9. [DeepAR](https://arxiv.org/abs/1704.0411
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:abf08f0b409cbfbf, topic:deep-learning, topic:pytorch
matched fp:abf08f0b409cbfbf, topic:transformer