Package: prophet 1.1.6

Sean Taylor

prophet: Automatic Forecasting Procedure

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.

Authors:Sean Taylor [cre, aut], Ben Letham [aut]

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prophet.pdf |prophet.html✨
prophet/json (API)

# Install 'prophet' in R:
install.packages('prophet', repos = c('https://community.r-multiverse.org', 'https://cloud.r-project.org'))

Peer review:

Bug tracker:https://github.com/facebook/prophet/issues

Uses libs:
  • c++– GNU Standard C++ Library v3

On CRAN:

forecastingpythoncpp

15.33 score 19k stars 12 packages 968 scripts 11k downloads 2 mentions 16 exports 88 dependencies

Last updated 3 months agofrom:2a57e9d342 (on v1.1.6-patched-pypi). Checks:OK: 1 NOTE: 8. Indexed: no.

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Doc / VignettesOKDec 02 2024
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R-4.5-linux-x86_64NOTEDec 02 2024
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Exports:add_changepoints_to_plotadd_country_holidaysadd_regressoradd_seasonalitycross_validationdyplot.prophetfit.prophetgenerated_holidaysmake_future_dataframeperformance_metricsplot_cross_validation_metricplot_forecast_componentpredictive_samplesprophetprophet_plot_componentsregressor_coefficients

Dependencies:abindbackportsbase64encBHbslibcachemcallrcheckmateclicolorspacecpp11descdigestdistributionaldplyrdygraphsevaluateextraDistrfansifarverfastmapfontawesomefsgenericsggplot2gluegridExtragtablehighrhtmltoolshtmlwidgetsinlineisobandjquerylibjsonliteknitrlabelinglatticelifecycleloolubridatemagrittrMASSMatrixmatrixStatsmemoisemgcvmimemunsellnlmenumDerivpillarpkgbuildpkgconfigposteriorprocessxpspurrrQuickJSRR6rappdirsRColorBrewerRcppRcppEigenRcppParallelrlangrmarkdownrstanrstantoolssassscalesStanHeadersstringistringrtensorAtibbletidyrtidyselecttimechangetinytexutf8vctrsviridisLitewithrxfunxtsyamlzoo

Quick Start Guide to Using Prophet

Sean J. Taylor and Ben Letham

Rendered fromquick_start.Rmdusingknitr::rmarkdownon Dec 02 2024.

Last update: 2018-06-02
Started: 2017-02-22

Readme and manuals

Help Manual

Help pageTopics
Get layers to overlay significant changepoints on prophet forecast plot.add_changepoints_to_plot
Add in built-in holidays for the specified country.add_country_holidays
Add an additional regressor to be used for fitting and predicting.add_regressor
Add a seasonal component with specified period, number of Fourier components, and prior scale.add_seasonality
Cross-validation for time series.cross_validation
Plot the prophet forecast.dyplot.prophet
Fit the prophet model.fit.prophet
Generated table of holiday dates at the country level from 1995 to 2045generated_holidays
Make dataframe with future dates for forecasting.make_future_dataframe
Compute performance metrics from cross-validation results.performance_metrics
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_cross_validation_metric
Plot a particular component of the forecast.plot_forecast_component
Plot the prophet forecast.plot.prophet
Predict using the prophet model.predict.prophet
Sample from the posterior predictive distribution.predictive_samples
Prophet forecaster.prophet
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.prophet_plot_components
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.regressor_coefficients
Compute a rolling median of x, after first aggregating by hrolling_median_by_h