Package: prophet 1.1.5

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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Peer review:

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

On CRAN:

forecastingpython

16 exports 18.3k stars 18.19 score 88 dependencies 12 dependents 2 mentions 908 scripts 12.2k downloads

Last updated 4 months agofrom:36421b70f0. Checks:OK: 1 NOTE: 8. Indexed: yes.

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