prophet

# Prophet: Automatic Forecasting Procedure

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**2026 Update:** Prophet is in maintenance mode as of v1.4.0. Only bug fixes, dependency bumps, and changes to the R package to meet parity with Python will be accepted. No new features are planned.

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**2023 Update:** We discuss our plans for the future of Prophet in this blog post: [facebook/prophet in 2023 and beyond](https://medium.com/@cuongduong_35162/facebook-prophet-in-2023-and-beyond-c5086151c138)

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Prophet is a procedure for forecasting time series data based on an additive model where non-linear trends are fit with yearly, weekly, and daily seasonality, plus holiday effects. It works best with time series that have strong seasonal effects and several seasons of historical data. Prophet is robust to missing data and shifts in the trend, and typically handles outliers well.

Prophet is [open source software](https://code.facebook.com/projects/) released by Facebook's [Core Data Science team](https://research.fb.com/category/data-science/). It is available for download on [CRAN](https://cran.r-project.org/package=prophet) and [PyPI](https://pypi.python.org/pypi/prophet/).

## Important links

- Homepage: https://facebook.github.io/prophet/
- HTML documentation: https://facebook.github.io/prophet/docs/quick_start.html
- Issue tracker: https://github.com/facebook/prophet/issues
- Source code repository: https://github.com/facebook/prophet
- Contributing: https://facebook.github.io/prophet/docs/contributing.html
- Prophet R package: https://cran.r-project.org/package=prophet
- Prophet Python package: https://pypi.python.org/pypi/prophet/
- Release blogpost: https://research.facebook.com/blog/2017/2/prophet-forecasting-at-scale/
- Prophet paper: Sean J. Taylor, Benjamin Letham (2018) Forecasting at scale. The American Statistician 72(1):37-45 (https://peerj.com/preprints/3190.pdf).
- Helter Skelter, simplified Prophet reimplementation, written in Scala/Apache Spark, optimized for distributed computing: https://github.com/AmadeusITGroup/HelterSkelter

## Installation in R - CRAN

⚠️ **The CRAN version of prophet i