Package: prophet 1.2.2

Cuong Duong

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:Cuong Duong [cre, aut], Sean Taylor [aut], Ben Letham [aut]

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manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
prophet/json (API)

# Install 'prophet' in R:

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

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

Uses libs:

c++– GNU Standard C++ Library v3

On CRAN:

forecasting python cpp

15.84 score 20k stars 14 packages 1.6k scripts 8.4k downloads 2 mentions 16 exports 84 dependencies

Last updated from:c01a23a2f5 (on v1.3.0). Checks:11 NOTE, 1 OK, 1 FAIL. Indexed: no.

Target	Result	Time
linux-devel-arm64	NOTE	283
linux-devel-x86_64	NOTE	288
source / vignettes	OK	327
linux-release-arm64	NOTE	277
linux-release-x86_64	NOTE	330
macos-release-arm64	NOTE	542
macos-release-x86_64	NOTE	449
macos-oldrel-arm64	NOTE	418
macos-oldrel-x86_64	NOTE	455
windows-devel	NOTE	286
windows-release	NOTE	283
windows-oldrel	NOTE	276
wasm-release	FAIL	205

Exports:add_changepoints_to_plot add_country_holidays add_regressor add_seasonality cross_validation dyplot.prophet fit.prophet generated_holidays make_future_dataframe performance_metrics plot_cross_validation_metric plot_forecast_component predictive_samples prophet prophet_plot_components regressor_coefficients

Dependencies:abind backports base64enc BH bslib cachem callr checkmate cli cpp11 desc digest distributional dplyr dygraphs evaluate extraDistr farver fastmap fontawesome fs generics ggplot2 glue gridExtra gtable highr htmltools htmlwidgets inline isoband jquerylib jsonlite knitr labeling lattice lifecycle loo lubridate magrittr matrixStats memoise mime numDeriv otel pillar pkgbuild pkgconfig posterior processx ps purrr QuickJSR R6 rappdirs RColorBrewer Rcpp RcppArmadillo RcppEigen RcppParallel rlang rmarkdown rstan rstantools S7 sass scales StanHeaders stringi stringr tensorA tibble tidyr tidyselect timechange tinytex utf8 vctrs viridisLite withr xfun xts yaml zoo

Quick Start Guide to Using Prophet

Rendered fromquick_start.Rmdusingknitr::rmarkdown

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

Citation

Development and contributors

Readme and manuals

Help Manual

Help page	Topics
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 2045	generated_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 h	rolling_median_by_h