AIStream-Peelout/flow-forecast

Health v2 · maintenance only

Adoption not part of the score

2292 stars · 300 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

Flow Forecast is a PyTorch-based deep learning framework for time series forecasting, classification, and anomaly detection. It offers state-of-the-art models (transformers, LSTMs, GRUs, ODEs), interpretability metrics, cloud integration, and model serving, originally built for flood forecasting.

Use cases

• forecast river flow and floods with deep learning
• train transformer models on multivariate time series
• detect anomalies in time series data
• apply transfer learning to time series regression
• benchmark state-of-the-art time series models
• serve trained forecasting models in production

When to choose

• you need a PyTorch framework specifically for time series forecasting
• you want transformer-based time series models out of the box
• you need interpretability metrics and model serving in one framework

When to avoid

• you need classical statistical forecasting like ARIMA or Prophet
• you work outside Python or PyTorch ecosystems
• you need a actively developed project with frequent updates

Facets

library · maturity maintenance

deep-learning machine-learning benchmarking time-series machine-learning deep-learning python pytorch time-series-forecasting transformer lstm anomaly-detection flood-forecasting

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markdown · JSON · MCP: product_card(name="AIStream-Peelout/flow-forecast")