R: Automatic Forecasting Procedure

# Automatic Forecasting Procedure

## Documentation for package ‘prophet’ version 1.0

- DESCRIPTION file.
- User guides, package vignettes and other documentation.

## 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