# facebook/prophet

Tool for producing high quality forecasts for time series data that has multiple seasonality with linear or non-linear growth.

Repository: https://github.com/facebook/prophet
Canonical: https://ross.abutalabs.com/products/prophet
Homepage: https://facebook.github.io/prophet
Language: Python
License: MIT
License Family: permissive
Topics: forecasting, r, python
Last push: 2026-08-15T03:38:56+00:00

## Health v2 (maintenance only)
Score: 98/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 97, release rhythm 98, longevity 100
- inputs: {"age_days": 3578, "days_push": 18, "days_rel": 18, "gap_med": 8.0, "n_releases_24m": 11}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 20369, forks 4635 (observed 2026-08-28T04:11:30.037926+00:00)

## What it is
Prophet is an automatic forecasting procedure for time series data, based on an additive model with yearly, weekly, and daily seasonality plus holiday effects, implemented in Python and R with shared Stan fitting code. It is robust to missing data, outliers, and trend shifts, and is now in maintenance mode with only bug fixes accepted.

## Use cases
- forecast sales or demand with strong weekly and yearly seasonality
- produce automatic time series forecasts from messy data with missing values and outliers
- forecast website traffic or metrics with holiday effects
- tune interpretable forecasting models with domain knowledge
- quickly generate baseline forecasts for planning and goal setting

## When to choose
- your time series has multiple seasonalities and at least several seasons of history
- you want fast, automated, tunable forecasts in Python or R without deep ML expertise
- your data has missing values, outliers, or trend shifts

## When to avoid
- you need new features or cutting-edge forecasting methods, since Prophet is in maintenance mode
- your series has strong covariate dependencies or requires deep learning models
- you need multivariate forecasting or heavy exogenous regressor support

## Facets
- artifact type: library
- maturity: maintenance
- function: machine-learning, data-science, analytics
- domain: data-science, time-series, analytics
- platform: python, cross-platform
- tags: forecasting, time-series, seasonality, stan, r-package, additive-model, r

## Member repositories
- facebook/prophet (main) score 98

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:11:30.037926+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-29T16:59:01.278666+00:00, confidence not recorded.
  - readme: https://github.com/facebook/prophet (fetched 2026-08-28T04:11:30.037926+00:00, sha 0ea6e4122061)
  - homepage: https://facebook.github.io/prophet (fetched 2026-08-29T07:57:31.048275+00:00, sha 2028ac22b1a5)
  - registry_pypi: https://pypi.org/pypi/prophet/json (fetched 2026-08-29T07:57:31.057360+00:00, sha 119d2f08d6ca)
- Data as of 2026-08-30T08:39:29.467469+00:00.