An Introduction to the Prophet Model: Time Series Forecasting Made Easy

Time series forecasting is an essential task in many industries, from finance to retail, where predicting future trends can guide critical business decisions. Traditional methods like ARIMA or newer deep learning techniques can be effective but often require significant tuning and expertise to get right. Enter Prophet, an open-source forecasting tool developed by Facebook that aims to simplify this process. Designed with business analysts and data scientists in mind, Prophet provides a powerful yet easy-to-use framework for time series forecasting, especially when dealing with complex seasonality and missing data.

What is Prophet?

Prophet is a forecasting model developed by Facebook’s Core Data Science team. It is designed to handle time series data with daily observations that display strong seasonal effects, non-linear growth trends, and multiple seasonalities (such as daily, weekly, and yearly cycles). One of Prophet’s key strengths is its ability to handle missing data and outliers gracefully, making it highly adaptable to real-world data scenarios.

The model is based on an additive approach, where the components (trend, seasonality, holidays) are combined to make the forecast. Prophet decomposes the time series into these components: