zhouhaoyi/Informer2020

Health v2 · maintenance only

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6516 stars · 1297 forks observed · 2026-08-28

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

The official PyTorch implementation of Informer, an efficient Transformer architecture for long sequence time-series forecasting that won the AAAI 2021 Best Paper award. It introduces ProbSparse attention to select active queries and enable efficient forecasting on very long time series.

Use cases

• forecast long time series with transformers
• implement ProbSparse attention in PyTorch
• reproduce Informer paper experiments
• benchmark long sequence forecasting models
• predict multivariate time series like electricity or traffic
• learn efficient transformer architectures for time series

When to choose

• you need to forecast long time series sequences with a transformer model
• you want to reproduce or build on the Informer AAAI'21 paper
• you need a PyTorch baseline for long sequence time-series forecasting research

When to avoid

• you need a production-ready forecasting service rather than research code
• you want actively developed features or broad model support beyond Informer
• you need a general-purpose time series library with many model options

Facets

library · maturity maintenance

deep-learning machine-learning deep-learning time-series machine-learning python transformer time-series-forecasting self-attention probsparse-attention pytorch research-code aaai-2021 gpu

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