An automatic forecasting procedure for time series data with multiple seasonality and linear or non-linear growth.
Prophet is an open-source forecasting library for time series data developed by Facebook. It uses an additive model to decompose data into trend, seasonal, and holiday components, enabling accurate predictions even with messy, real-world datasets. The tool is designed to handle multiple seasonality patterns and non-linear growth, making it particularly useful for business forecasting, demand planning, and research applications.
Data scientists, analysts, and developers who need to generate reliable forecasts from time series data, especially those working with business metrics, economic indicators, or any data exhibiting strong seasonal patterns.
Prophet stands out for its ease of use, robustness to data imperfections, and automatic handling of complex seasonality and holiday effects. Unlike many statistical forecasting tools, it requires minimal configuration and provides interpretable results, making advanced forecasting accessible without deep expertise in time series analysis.
Tool for producing high quality forecasts for time series data that has multiple seasonality with linear or non-linear growth.
Open-Awesome is built by the community, for the community. Submit a project, suggest an awesome list, or help improve the catalog on GitHub.
Prophet automatically models yearly, weekly, and daily seasonal patterns using Fourier series, as per its additive model, reducing manual configuration for common business cycles.
It tolerates missing data, trend shifts, and outliers well, making it reliable for messy real-world datasets, a key philosophy highlighted in the documentation.
The tool incorporates known holidays and events to improve forecast accuracy, with support from the holidays package, simplifying inclusion of external factors.
Includes functions like cross_validate for model validation and performance evaluation, helping users assess forecast reliability without external tools.
For R, the CRAN version is outdated, and installing the latest release requires GitHub and backend tweaks; on Windows, compiler setups add friction, as noted in the README.
Prophet relies on Stan for model fitting, which can be resource-intensive—requiring at least 4GB memory on Linux—and adds bloat compared to lightweight alternatives.
The additive/multiplicative model structure may not capture complex, non-linear interactions beyond trend and seasonality, making it less suited for intricate time series patterns.