#3939th of 168 in ML frameworks and librariesFor data and documents, data analysis
prophet
Additive time series forecasting library for Python and R
- Stars
- 20.5k
- License
- MIT
- Last commit
- Oct 2026
- Last release
- Oct 2026
- Language
- Python
20,034 on 21 Apr 202620,480 on 10 Nov 2026
From GitHub's daily star counts.
Checked on GitHub.
Overview
Prophet fits an additive model to time series, combining a non-linear trend with yearly, weekly and daily seasonality and holiday effects. It runs on Stan (cmdstan via cmdstanpy in Python, rstan in R) and includes cross-validation, performance metrics and plotting. It suits series with strong seasonality and several seasons of history. The project has been in maintenance mode since v1.4.0.
Who it is for: Data scientists forecasting seasonal business time series in Python or R
Strengths
- Python (PyPI, conda-forge) and R packages with matching APIs
- Handles missing data, trend shifts and holiday effects
- Built-in cross-validation, performance metrics and custom metrics
- Pre-built Python wheels bundle model binaries, including macOS arm64
Weaknesses
- Maintenance mode since v1.4.0: bug fixes only, no new features planned
- CRAN release is outdated; latest R version installs from GitHub
- Installing from source needs a C++ compiler and at least 4GB memory
- Suited to series with strong seasonality and several seasons of history
What it needs
- RAM ≥ 2 GB
- no GPU
- Docker + Compose
- Needs cmdstan, cmdstanpy, rstan, C++ compiler
- Models: additive time series model (Stan)
Quick answers
Is prophet open source?
Yes. Its code is on GitHub under the MIT license.
Can you run prophet with Docker?
Yes. Its repository has a Docker setup: a Dockerfile, a Compose file or a published image.
Does prophet need a GPU?
No, it runs without one. Plan for at least 2 GB of RAM.
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