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Prophet is a procedure for forecasting time series data based on an additive model where non-linear trends are fit with yearly, weekly, and daily seasonality, plus holiday effects. It works best with time series that have strong seasonal effects and several seasons of historical data. Prophet is robust to missing data and shifts in the trend, and typically handles outliers well. Prophet is used in many applications across Facebook for producing reliable forecasts for planning and goal setting. We’ve found it to perform better than any other approach in the majority of cases. We fit models in Stan so that you get forecasts in just a few seconds. Get a reasonable forecast on messy data with no manual effort. Prophet is robust to outliers, missing data, and dramatic changes in your time series.
Features
- The Prophet procedure includes many possibilities for users to tweak and adjust forecasts
- You can use human-interpretable parameters to improve your forecast by adding your domain knowledge
- We’ve implemented the Prophet procedure in R and Python
- Use whatever language you’re comfortable with to get forecasts
- Prophet follows the sklearn model API, so we create an instance of the Prophet class and then call its fit and predict methods
- As with most modeling procedures in R, we use the generic predict function to get our forecast
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