awslabs/gluonts

Health v2 · maintenance only

Adoption not part of the score

5227 stars · 830 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded

GluonTS is a Python library for probabilistic time series modeling, focused on deep learning based forecasting models built on PyTorch. It provides dataset handling, model training, and evaluation utilities for time series prediction tasks.

Use cases

• forecast future values of a time series with uncertainty estimates
• train a DeepAR model on my sales data
• probabilistic demand forecasting in Python
• evaluate and compare time series forecasting models
• train deep learning models on multiple related time series
• generate probabilistic predictions from pandas DataFrames

When to choose

• you need probabilistic (distributional) forecasts rather than point predictions
• you want ready-to-use deep learning forecasting models like DeepAR with PyTorch
• you need tooling for datasets, splitting, and evaluation of time series experiments

When to avoid

• you only need simple classical methods like ARIMA or exponential smoothing
• you need a lightweight point-forecast library without deep learning dependencies
• you need zero-shot forecasting with pretrained foundation models (consider Chronos instead)

Facets

library · maturity stable

machine-learning deep-learning data-science machine-learning data-science time-series artificial-intelligence python time-series forecasting probabilistic-forecasting pytorch deepar neural-networks

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