Prophet is Facebook's 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.
Usage
prophesize(
df,
n_future = 60,
country = NULL,
trend.param = 0.05,
logged = FALSE,
pout = 0.03,
project = "Prophet Forecast"
)Arguments
- df
Data frame. Must contain date/time column and values column, in that order.
- n_future
Integer. How many steps do you wish to forecast?
- country
Character. Country code for holidays.
- trend.param
Numeric. Flexibility of trend component. Default is 0.05, and as this value becomes larger, the trend component will be more flexible.
- logged
Boolean. Convert values into logs?
- pout
Numeric. Get rid of pout % of outliers.
- project
Character. Name of your forecast project for plot title
Details
Official documentation: https://github.com/facebook/prophet
See also
Other Forecast:
forecast_arima()