// the find
facebook/prophet
Tool for producing high quality forecasts for time series data that has multiple seasonality with linear or non-linear growth.
Prophet is Facebook's time series forecasting library built on an additive model with piecewise linear/logistic trend, Fourier-series seasonality, and holiday effects. It's aimed at analysts and data scientists who need interpretable forecasts without deep statistics knowledge. Both Python and R are supported, with Stan doing the heavy lifting under the hood.
The decomposable model (trend + seasonality + holidays) means you can actually inspect what's driving each component — not a black box. Handling of missing data and outliers is genuinely good without manual intervention. The changepoint detection is automatic but also manually overridable, which is the right trade-off. Cross-validation tooling with time-series-aware cutoffs is built in and works correctly, which most DIY implementations get wrong.
Accuracy on non-business time series (anything without strong weekly/yearly seasonality) is often mediocre — ARIMA or even simple exponential smoothing will beat it. The Stan dependency makes installation painful on Windows and adds ~4GB RAM overhead at fit time. The project is effectively in maintenance mode: the 2023 blog post explicitly said Meta isn't actively developing it further, so you're adopting a frozen library. Uncertainty intervals assume the future looks like the past in ways that fail badly at structural breaks — the model will confidently forecast through COVID-style disruptions as if they're outliers.