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Autoformer

Autoformer is an open-source deep-learning model and code release for long-term time-series forecasting, built around progressive trend-seasonal decomposition and a series-wise Auto-Correlation mechanism.

Independent OpenViST project profile, based on linked sources.

Autoformer — OpenViST project profile in AI Models.
OpenViST project illustration. See the linked sources for product details.
Understand the project

Practical overview

When it fits

Use Autoformer when you need a code-based starting point for long-horizon time-series forecasting and can work with its Python and PyTorch setup.

Who it is for

  • Researchers reproducing long-term forecasting benchmarks.
  • Practitioners adapting a forecasting model to business or operational time series.
  • Teams comparing forecasting approaches on energy, traffic, economics, weather or disease-related data.

Documented uses

  • Train the model with the repository's supplied benchmark experiment scripts.
  • Reproduce benchmark forecasting experiments using the provided data guidance.
  • Use the architecture as a research prototype or forecasting baseline.
Before you choose

Requirements & limits

The repository is public and identifies its license as MIT.

  • The documented setup specifies Python 3.6 and PyTorch 1.9.0.
  • The model's distinguishing design combines progressive trend-seasonal decomposition with series-wise Auto-Correlation.
  • The repository provides benchmark scripts and identifies a Chinese-language notebook as a simple prediction example.

Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.

Source-based practical guide

Set up the documented benchmark workflow

Prepare the repository's benchmark environment and datasets for running its forecasting experiment scripts.

Before you start

  • Python 3.6
  • PyTorch 1.9.0
  • A clone of the Autoformer repository

Steps

  1. Install Python 3.6 and PyTorch 1.9.0.

  2. Download benchmark datasets using the repository's dataset instructions.

  3. Choose a supplied experiment script under the scripts directory.

Expected outcome

The documented software prerequisites and data are in place to proceed with a supplied benchmark script.

Guide limitations

  • These setup steps do not establish that an experiment has run successfully.
  • The repository also describes an alternative Docker, conda and Make workflow.
Evidence and transparency

Sources & review

Source record details
Source availability
Active
Sources checked
15 October 2026
Original website title
thuml/Autoformer · Official homepage title · retained in its source language