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prophet (version 0.4)

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.

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Install

install.packages('prophet')

Monthly Downloads

12,438

Version

0.4

License

BSD_3_clause + file LICENSE

Issues

437

Pull Requests

10

Stars

19,161

Forks

4,578

Maintainer

Sean Taylor

Last Published

December 21st, 2018

Functions in prophet (0.4)

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.
fourier_series

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

Generate cutoff dates
mae

Mean absolute error
make_all_seasonality_features

Dataframe with seasonality features. Includes seasonality features, holiday features, and added regressors.
cross_validation

Cross-validation for time series.
plot_forecast_component

Plot a particular component of the forecast.
plot_seasonality

Plot a custom seasonal component.
rolling_mean

Compute a rolling mean of x
df_for_plotting

Merge history and forecast for plotting.
generated_holidays

holidays table
coverage

Coverage
construct_holiday_dataframe

Construct a dataframe of holiday dates.
get_holiday_names

Return all possible holiday names of given country
linear_growth_init

Initialize linear growth.
sample_model

Simulate observations from the extrapolated generative model.
sample_posterior_predictive

Prophet posterior predictive samples.
sample_predictive_trend

Simulate the trend using the extrapolated generative model.
logistic_growth_init

Initialize logistic growth.
predict.prophet

Predict using the prophet model.
predict_seasonal_components

Predict seasonality components, holidays, and added regressors.
regressor_column_matrix

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

Root mean squared error
mape

Mean absolute percent error
validate_column_name

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

Make dataframe with future dates for forecasting.
validate_inputs

Validates the inputs to Prophet.
mse

Mean squared error
plot_weekly

Plot the weekly component of the forecast.
plot_yearly

Plot the yearly component of the forecast.
make_holiday_features

Construct a matrix of holiday features.
add_group_component

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

Copy Prophet object.
add_regressor

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

Prepare dataframe for plotting seasonal components.
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.
get_prophet_stan_model

Load compiled Stan model
set_auto_seasonalities

Set seasonalities that were left on auto.
initialize_scales_fn

Initialize model scales.
add_seasonality

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

Prepare dataframe for fitting or predicting.
compile_stan_model

Compile Stan model
time_diff

Time difference between datetimes
dyplot.prophet

Plot the prophet forecast.
fit.prophet

Fit the prophet model.
parse_seasonality_args

Get number of Fourier components for built-in seasonalities.
plot.prophet

Plot the prophet forecast.
performance_metrics

Compute performance metrics from cross-validation results.
make_seasonality_features

Data frame with seasonality features.
make_holidays_df

Make dataframe of holidays for given years and countries
piecewise_linear

Evaluate the piecewise linear function.
piecewise_logistic

Evaluate the piecewise logistic function.
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.
predictive_samples

Sample from the posterior predictive distribution.
prophet

Prophet forecaster.
predict_uncertainty

Prophet uncertainty intervals for yhat and trend
predict_trend

Predict trend using the prophet model.
set_changepoints

Set changepoints
set_date

Convert date vector