thuml/iTransformer

Health v2 · maintenance only

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2200 stars · 369 forks observed · 2026-08-28

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

Official PyTorch implementation of iTransformer, an ICLR 2024 Spotlight paper that inverts the Transformer architecture for multivariate time series forecasting by treating each variate as a token. It includes experiment scripts and achieves state-of-the-art results on standard forecasting benchmarks.

Use cases

• forecast multivariate time series with transformers
• reproduce iTransformer paper experiments
• benchmark forecasting models on time series datasets
• predict future values of multiple correlated sensors
• compare transformer baselines for long-term forecasting

When to choose

• you need a strong transformer-based multivariate forecasting baseline
• you want to reproduce or extend the iTransformer paper
• your data has many correlated variates and long lookback windows

When to avoid

• you need simple univariate forecasting where linear models suffice
• you need a production-ready forecasting service rather than research code
• you lack GPU resources for training transformer models

Facets

library · maturity stable

machine-learning deep-learning time-series machine-learning data-science python time-series-forecasting transformer research-code iclr-2024 multivariate-forecasting gpu

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