Learn R Programming
⚠️There's a newer version (1.0) of this package.Take me there.

prophet (version 0.3.0.1)

Automatic Forecasting Procedure

Description

Implements 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.

Copy Link

Version

Install

install.packages('prophet')

Monthly Downloads

11,597

Version

0.3.0.1

License

BSD_3_clause + file LICENSE

Maintainer

Sean Taylor

Last Published

June 15th, 2018

Functions in prophet (0.3.0.1)

dyplot.prophet

Plot the prophet forecast.
predict_seasonal_components

Predict seasonality components, holidays, and added regressors.
make_all_seasonality_features

Dataframe with seasonality features. Includes seasonality features, holiday features, and added regressors.
predict.prophet

Predict using the prophet model.
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.
piecewise_logistic

Evaluate the piecewise logistic function.
sample_posterior_predictive

Prophet posterior predictive samples.
plot.prophet

Plot the prophet forecast.
predictive_samples

Sample from the posterior predictive distribution.
mape

Mean absolute percent error
predict_trend

Predict trend using the prophet model.
regressor_column_matrix

Dataframe indicating which columns of the feature matrix correspond to which seasonality/regressor components.
sample_model

Simulate observations from the extrapolated generative model.
plot_yearly

Plot the yearly component of the forecast.
set_date

Convert date vector
setup_dataframe

Prepare dataframe for fitting or predicting.
prophet

Prophet forecaster.
parse_seasonality_args

Get number of Fourier components for built-in seasonalities.
mse

Mean squared error
prophet_copy

Copy Prophet object.
predict_uncertainty

Prophet uncertainty intervals for yhat and trend
rmse

Root mean squared error
make_future_dataframe

Make dataframe with future dates for forecasting.
set_changepoints

Set changepoints
rolling_mean

Compute a rolling mean of x
performance_metrics

Compute performance metrics from cross-validation results.
set_auto_seasonalities

Set seasonalities that were left on auto.
plot_seasonality

Plot a custom seasonal component.
make_holiday_features

Construct a matrix of holiday features.
plot_weekly

Plot the weekly component of the forecast.
piecewise_linear

Evaluate the piecewise linear function.
sample_predictive_trend

Simulate the trend using the extrapolated generative model.
seasonality_plot_df

Prepare dataframe for plotting seasonal components.
time_diff

Time difference between datetimes
validate_column_name

Validates the name of a seasonality, holiday, or regressor.
validate_inputs

Validates the inputs to Prophet.
add_seasonality

Add a seasonal component with specified period, number of Fourier components, and prior scale.
add_regressor

Add an additional regressor to be used for fitting and predicting.
add_changepoints_to_plot

Get layers to overlay significant changepoints on prophet forecast plot.
fit.prophet

Fit the prophet model.
df_for_plotting

Merge history and forecast for plotting.
compile_stan_model

Compile Stan model
add_group_component

Adds a component with given name that contains all of the components in group.
coverage

Coverage
initialize_scales_fn

Initialize model scales.
cross_validation

Cross-validation for time series.
linear_growth_init

Initialize linear growth.
get_prophet_stan_model

Load compiled Stan model
generate_cutoffs

Generate cutoff dates
fourier_series

Provides Fourier series components with the specified frequency and order.
mae

Mean absolute error
make_seasonality_features

Data frame with seasonality features.
logistic_growth_init

Initialize logistic growth.
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.