facebook/prophet
Tool for producing high quality forecasts for time series data that has multiple seasonality with linear or non-linear growth. observed · 2026-08-28
Health v2 · maintenance only
98/100
- Activity 97
- Release rhythm 98
- Longevity 100
How is this computed?
round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10) — computed 2026-09-03. Adoption (stars, forks) is never an input.
- gap_med: 8.0
- age_days: 3578
- days_rel: 18
- days_push: 18
- n_releases_24m: 11
Adoption not part of the score
20369 stars · 4635 forks observed · 2026-08-28
What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded
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
library · maturity maintenance
machine-learning data-science analytics data-science time-series analytics python cross-platform forecasting time-series seasonality stan r-package additive-model r
3 sources
- readme: https://github.com/facebook/prophet · fetched 2026-08-28 · 0ea6e4122061
- homepage: https://facebook.github.io/prophet · fetched 2026-08-29 · 2028ac22b1a5
- registry_pypi: https://pypi.org/pypi/prophet/json · fetched 2026-08-29 · 119d2f08d6ca
Member repositories
| Repository | Role | Health v2 |
|---|---|---|
| facebook/prophet | main | 98 |
For agents
markdown · JSON · MCP: product_card(name="facebook/prophet")
Data as of 2026-08-30T08:39:29.467469+00:00 · Report a problem