Forecasting platforms & libraries · Meta (Facebook Core Data Science)

Prophet

Open-source forecasting library designed to be easy to use on business time series with holidays and seasonality.

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Prophet is an open-source forecasting library built by Meta's data science team to make reasonable forecasts easy to produce with minimal tuning. It fits an additive model of trend, yearly/weekly/daily seasonality and user-supplied holiday effects, and is deliberately forgiving of missing data, outliers and trend changes — the target audience is analysts who need a quick, interpretable forecast rather than machine-learning specialists tuning hyperparameters. It is available for Python and R. Its weakness is the flip side of its simplicity: Prophet's additive seasonal structure handles a single dominant seasonal pattern well but struggles with complex, multiple or interacting seasonalities and with large-scale batch forecasting compared to newer statistical or deep-learning libraries. It remains a common default for a first forecast or a baseline to beat.

At a glance

Vendor Meta (Facebook Core Data Science)
Pricing model Open source + paid options
Free tier Yes
Deployment Self-hosted
Open source Yes (MIT)
Best for Analysts who need a fast, interpretable forecast on a single time series with clear seasonality and holidays.

Pricing

Free and open source with no commercial tier.

Pricing has not been verified yet — see the vendor's site.

Features

  • Additive model of trend, seasonality and holiday effects
  • Automatic handling of missing data and outliers
  • Custom holiday and event regressors
  • Uncertainty intervals on forecasts
  • Python and R implementations
  • Automatic changepoint detection for trend shifts
  • Minimal parameter tuning required for a first forecast

Integrations

Profile last reviewed September 21, 2026

Head to head

Alternatives

Nixtla Open-source statistical/ML forecasting libraries plus TimeGPT, a hosted foundation model for time series. Darts Open-source Python library offering one unified API across dozens of classical, ML and deep-learning forecasting models. sktime Open-source Python toolkit that extends scikit-learn conventions to forecasting, classification and clustering of time series.

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Terms to know

Time series forecasting Predicting future values of a metric using patterns found in its own historical sequence over time. Demand forecasting Estimating future customer demand for a product so inventory, production and staffing can be planned ahead. Predictive analytics Analytics that uses historical data and statistical models to estimate what is likely to happen next.

Related guides

How to choose a forecasting platform or library Pick a time-series forecasting library by how many series you're forecasting and how much modeling control your team wants. How to forecast demand Baselines worth beating, where seasonality and model choice actually matter, and how to judge accuracy honestly. Time series analysis, explained How analysts model data ordered in time — the patterns forecasting methods look for, the pitfalls, and where the data actually lives.