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Example image 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) 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 forecasting framework. Currently, Task-TS from CoronaWhy primarily maintains this repository. Pull requests are welcome. Historically, this repository provided open-source benchmarks and codes for flash flood and river flow forecasting. Full transformer (SimpleTransformer in model_dict): The full original transformer with all 8 encoder and decoder blocks. Requires passing the target in at inference.
Features
- Vanilla LSTM (LSTM): A basic LSTM that is suitable for multivariate time series forecasting and transfer learning
- Simple Multi-Head Attention (MultiHeadSimple): A simple multi-head attention block and linear embedding layers. Suitable for transfer learning
- 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
- DA-RNN: (DARNN) A well rounded model with which utilizes a LSTM + attention
- Simple Linear Model (SimpleLinearModel): Essentially a linear regression model with a few more layers. Great for rapid prototyping of features and interpretability
- Transformer XL: Porting Transformer XL for time series
Project Samples
