yuqinie98/PatchTST

Health v2 · maintenance only

Flags: no_releases

Adoption not part of the score

2685 stars · 450 forks observed · 2026-08-28

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

Official PyTorch implementation of PatchTST, an ICLR 2023 Transformer model for long-term time series forecasting based on patching and channel-independence. It includes code for both supervised and self-supervised (pre-training/transfer) learning experiments.

Use cases

• forecast long-term time series with transformers
• reproduce PatchTST ICLR 2023 paper results
• train a self-supervised pretrained model for time series and transfer it to downstream tasks
• benchmark forecasting models like DLinear against PatchTST on datasets like ETT and weather
• experiment with patching and channel-independence designs for multivariate forecasting

When to choose

• you need state-of-the-art long-term time series forecasting in Python
• you want a research reference implementation of PatchTST
• you want to pretrain on time series and fine-tune for downstream forecasting

When to avoid

• you need a production-ready forecasting library with maintained APIs - use GluonTS, NeuralForecast, or tsai which integrate PatchTST
• you need short-horizon or non-neural forecasting methods
• you need real-time streaming forecasting

Facets

library · maturity stable

machine-learning deep-learning time-series machine-learning deep-learning data-science python time-series-forecasting transformer patchtst research-code self-supervised-learning long-term-forecasting

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