mirror of
https://github.com/saymrwulf/prophet.git
synced 2026-05-26 22:35:48 +00:00
89 lines
3.4 KiB
R
89 lines
3.4 KiB
R
% Generated by roxygen2: do not edit by hand
|
|
% Please edit documentation in R/prophet.R
|
|
\name{prophet}
|
|
\alias{prophet}
|
|
\title{Prophet forecaster.}
|
|
\usage{
|
|
prophet(df = NULL, growth = "linear", changepoints = NULL,
|
|
n.changepoints = 25, yearly.seasonality = "auto",
|
|
weekly.seasonality = "auto", daily.seasonality = "auto",
|
|
holidays = NULL, seasonality.prior.scale = 10,
|
|
holidays.prior.scale = 10, changepoint.prior.scale = 0.05,
|
|
mcmc.samples = 0, interval.width = 0.8, uncertainty.samples = 1000,
|
|
fit = TRUE, ...)
|
|
}
|
|
\arguments{
|
|
\item{df}{(optional) Dataframe containing the history. Must have columns ds
|
|
(date type) and y, the time series. If growth is logistic, then df must
|
|
also have a column cap that specifies the capacity at each ds. If not
|
|
provided, then the model object will be instantiated but not fit; use
|
|
fit.prophet(m, df) to fit the model.}
|
|
|
|
\item{growth}{String 'linear' or 'logistic' to specify a linear or logistic
|
|
trend.}
|
|
|
|
\item{changepoints}{Vector of dates at which to include potential
|
|
changepoints. If not specified, potential changepoints are selected
|
|
automatically.}
|
|
|
|
\item{n.changepoints}{Number of potential changepoints to include. Not used
|
|
if input `changepoints` is supplied. If `changepoints` is not supplied,
|
|
then n.changepoints potential changepoints are selected uniformly from the
|
|
first 80 percent of df$ds.}
|
|
|
|
\item{yearly.seasonality}{Fit yearly seasonality. Can be 'auto', TRUE,
|
|
FALSE, or a number of Fourier terms to generate.}
|
|
|
|
\item{weekly.seasonality}{Fit weekly seasonality. Can be 'auto', TRUE,
|
|
FALSE, or a number of Fourier terms to generate.}
|
|
|
|
\item{daily.seasonality}{Fit daily seasonality. Can be 'auto', TRUE,
|
|
FALSE, or a number of Fourier terms to generate.}
|
|
|
|
\item{holidays}{data frame with columns holiday (character) and ds (date
|
|
type)and optionally columns lower_window and upper_window which specify a
|
|
range of days around the date to be included as holidays. lower_window=-2
|
|
will include 2 days prior to the date as holidays.}
|
|
|
|
\item{seasonality.prior.scale}{Parameter modulating the strength of the
|
|
seasonality model. Larger values allow the model to fit larger seasonal
|
|
fluctuations, smaller values dampen the seasonality.}
|
|
|
|
\item{holidays.prior.scale}{Parameter modulating the strength of the holiday
|
|
components model.}
|
|
|
|
\item{changepoint.prior.scale}{Parameter modulating the flexibility of the
|
|
automatic changepoint selection. Large values will allow many changepoints,
|
|
small values will allow few changepoints.}
|
|
|
|
\item{mcmc.samples}{Integer, if greater than 0, will do full Bayesian
|
|
inference with the specified number of MCMC samples. If 0, will do MAP
|
|
estimation.}
|
|
|
|
\item{interval.width}{Numeric, width of the uncertainty intervals provided
|
|
for the forecast. If mcmc.samples=0, this will be only the uncertainty
|
|
in the trend using the MAP estimate of the extrapolated generative model.
|
|
If mcmc.samples>0, this will be integrated over all model parameters,
|
|
which will include uncertainty in seasonality.}
|
|
|
|
\item{uncertainty.samples}{Number of simulated draws used to estimate
|
|
uncertainty intervals.}
|
|
|
|
\item{fit}{Boolean, if FALSE the model is initialized but not fit.}
|
|
|
|
\item{...}{Additional arguments, passed to \code{\link{fit.prophet}}}
|
|
}
|
|
\value{
|
|
A prophet model.
|
|
}
|
|
\description{
|
|
Prophet forecaster.
|
|
}
|
|
\examples{
|
|
\dontrun{
|
|
history <- data.frame(ds = seq(as.Date('2015-01-01'), as.Date('2016-01-01'), by = 'd'),
|
|
y = sin(1:366/200) + rnorm(366)/10)
|
|
m <- prophet(history)
|
|
}
|
|
|
|
}
|