{"categories":[{"label":"Information Analysis","url":"https://skillfed.io/packages/category/scientific-engineering-information-analysis"}],"enrichment":{"capability":"Prophet forecasts time series data using an additive model that combines non-linear trends with seasonal patterns (yearly, weekly, daily) and holiday effects, handling missing data and outliers robustly.","skillfed_tags":["forecasting","time-series","seasonality"],"use_cases":["Forecast sales, revenue, or demand over the next quarter or year for business planning and inventory management.","Predict website traffic, user engagement, or API request volumes to capacity-plan infrastructure.","Estimate energy consumption or utility demand by hour, day, or season for grid management.","Project staffing needs or call-center volume based on historical patterns with holiday adjustments.","Monitor and forecast metrics in monitoring systems where you need to detect anomalies relative to expected seasonal trends."],"what_it_does":"Prophet is a time-series forecasting library built by Facebook's Core Data Science team that automates the process of fitting trends and seasonal patterns to historical data. It decomposes a time series into trend, seasonality (yearly, weekly, daily), and holiday components, then uses an additive model to project future values. The library is designed to work well with data that has strong seasonal effects and multiple seasons of history, and it tolerates missing data and trend shifts gracefully.\n\nYou provide Prophet with a pandas DataFrame containing a time column ('ds') and a target column ('y'), call fit() to train the model, then use make_future_dataframe() and predict() to generate forecasts. The package depends on cmdstanpy (which wraps Stan, a probabilistic programming language), numpy, pandas, matplotlib, and a few utility libraries. Installation requires a C++ compiler because Stan code is compiled during setup, which adds medium friction but is a one-time cost.","worth_installing":"Yes, if you need automatic time-series forecasting with built-in seasonal and holiday handling. Prophet is well-maintained, actively used in production (7M+ monthly downloads), has no known vulnerabilities, and the MIT license poses no restrictions. The medium install friction (C++ compilation via cmdstanpy) is a one-time cost. Install it if your data has clear seasonal patterns and you want a library that requires minimal tuning; skip it if you need ultra-low latency inference or forecasting on non-seasonal data."},"id":"prophet","links":{"html":"https://skillfed.io/packages/prophet","md":"https://skillfed.io/packages/prophet.md","pypi":"https://pypi.org/project/prophet/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-01-27","license_spdx":null,"license_treatment":"permissive","name":"prophet","python_support":"supports_current","summary":"Automatic Forecasting Procedure"},"popularity":{"monthly_downloads":7034911,"position":1794,"tier":"top_5000"},"security":{"n_vulnerabilities":0},"version":"1.3.0"}