Automatic Forecasting Procedure

Documentation for package ‘prophet’ version 1.0

Help Pages

add_changepoints_to_plot	Get layers to overlay significant changepoints on prophet forecast plot.
add_country_holidays	Add in built-in holidays for the specified country.
add_regressor	Add an additional regressor to be used for fitting and predicting.
add_seasonality	Add a seasonal component with specified period, number of Fourier components, and prior scale.
cross_validation	Cross-validation for time series.
dyplot.prophet	Plot the prophet forecast.
fit.prophet	Fit the prophet model.
generated_holidays	holidays table
make_future_dataframe	Make dataframe with future dates for forecasting.
performance_metrics	Compute performance metrics from cross-validation results.
plot.prophet	Plot the prophet forecast.
plot_cross_validation_metric	Plot a performance metric vs. forecast horizon from cross validation. Cross validation produces a collection of out-of-sample model predictions that can be compared to actual values, at a range of different horizons (distance from the cutoff). This computes a specified performance metric for each prediction, and aggregated over a rolling window with horizon.
plot_forecast_component	Plot a particular component of the forecast.
predict.prophet	Predict using the prophet model.
predictive_samples	Sample from the posterior predictive distribution.
prophet	Prophet forecaster.
prophet_plot_components	Plot the components of a prophet forecast. Prints a ggplot2 with whichever are available of: trend, holidays, weekly seasonality, yearly seasonality, and additive and multiplicative extra regressors.
regressor_coefficients	Summarise the coefficients of the extra regressors used in the model. For additive regressors, the coefficient represents the incremental impact on 'y' of a unit increase in the regressor. For multiplicative regressors, the incremental impact is equal to 'trend(t)' multiplied by the coefficient.
rolling_median_by_h	Compute a rolling median of x, after first aggregating by h