Understand the project
Practical overview
When it fits
Use Aeon when a project needs a Python toolkit covering multiple time-series learning tasks, or when developers need to compare models and methods rather than adopt a single standalone application.
Who it is for
- Python developers building time-series machine-learning applications
- Data scientists working with scikit-learn-compatible workflows
- Researchers comparing classical and deep-learning time-series methods
Documented uses
- Forecast operational or demand time series
- Classify, regress or cluster time-series data
- Detect anomalies in time-series signals
- Find similar historical time-series segments
- Benchmark time-series models and methods
Before you choose
Requirements & limits
The repository is publicly available and licensed under BSD-3-Clause. The documented project is a Python library with scikit-learn compatibility; no account or pricing details are provided.
- Python is the primary implementation language.
- scikit-learn compatibility can fit existing Python machine-learning workflows.
- The scope includes both classical and deep-learning approaches.
- The project is a library, so application interfaces and surrounding production workflows must be built separately.
- Documentation, examples and an API reference are linked from the repository.
Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.
Evidence and transparency
Sources & review
Source record details
- Source availability
- Active
- Sources checked
- 3 October 2026
- Original website title
- aeon-toolkit/aeon · Official homepage title · retained in its source language