thuml/Autoformer

Health v2 · maintenance only

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2481 stars · 497 forks observed · 2026-08-28

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

Autoformer is the official PyTorch implementation of the NeurIPS 2021 paper 'Autoformer: Decomposition Transformers with Auto-Correlation for Long-Term Series Forecasting'. It provides a deep decomposition architecture with a series-wise Auto-Correlation mechanism for long-term time series forecasting across benchmarks in energy, traffic, economics, weather, and disease domains.

Use cases

• forecast long-term time series with transformers
• train a decomposition-based forecasting model on benchmark datasets
• predict energy load, traffic, or weather trends
• reproduce NeurIPS 2021 time series forecasting results
• compare Auto-Correlation attention against standard self-attention for forecasting
• run long-horizon forecasting experiments in PyTorch

When to choose

• you need state-of-the-art long-term time series forecasting with a proven Transformer variant
• you want a research-grade PyTorch codebase with pre-processed benchmark datasets
• your data has strong trend and seasonality components suited to decomposition
• you want a model with log-linear complexity instead of standard self-attention

When to avoid

• you need short-term forecasting, imputation, anomaly detection, or classification - use the authors' Time-Series-Library instead
• you need a production-ready forecasting service rather than research code
• you work outside Python/PyTorch ecosystems
• you need lightweight classical forecasting methods like ARIMA or Prophet

Facets

library · maturity stable

deep-learning machine-learning time-series deep-learning machine-learning python time-series-forecasting transformer auto-correlation research-code pytorch neurips-2021

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