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.

Version: 1.0
Depends: R (≥ 3.4.0), Rcpp (≥ 0.12.0), rlang (≥
Imports: dplyr (≥ 0.7.7), dygraphs (≥, extraDistr, ggplot2, grid, lubridate, methods, RcppParallel (≥ 5.0.1), rstan (≥ 2.18.1), rstantools (≥ 2.0.0), scales, StanHeaders, stats, tidyr (≥ 0.6.1), xts
LinkingTo: BH (≥ 1.66.0), Rcpp (≥ 0.12.0), RcppParallel (≥ 5.0.1), RcppEigen (≥, rstan (≥ 2.18.1), StanHeaders (≥ 2.18.0)
Suggests: knitr, testthat, readr, rmarkdown
Published: 2021-03-30
DOI: 10.32614/CRAN.package.prophet
Author: Sean Taylor [cre, aut], Ben Letham [aut]
Maintainer: Sean Taylor <sjtz at>
License: MIT + file LICENSE
NeedsCompilation: yes
SystemRequirements: GNU make, C++11
In views: MissingData, TimeSeries
CRAN checks: prophet results


Reference manual: prophet.pdf
Vignettes: Quick Start Guide to Using Prophet


Package source: prophet_1.0.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): prophet_1.0.tgz, r-oldrel (arm64): prophet_1.0.tgz, r-release (x86_64): prophet_1.0.tgz, r-oldrel (x86_64): prophet_1.0.tgz
Old sources: prophet archive

Reverse dependencies:

Reverse imports: autoTS, bayesforecast, fable.prophet, modeltime, promotionImpact, Robyn


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