## "Fossies" - the Fresh Open Source Software Archive

### Source code changes of the file "R/man/prophet.Rd" betweenprophet-1.0.tar.gz and prophet-1.1.tar.gz

About: Prophet is a tool for producing high quality forecasts for time series data that has multiple seasonality with linear or non-linear growth.

prophet.Rd  (prophet-1.0):prophet.Rd  (prophet-1.1)
skipping to change at line 25 skipping to change at line 25
daily.seasonality = "auto", daily.seasonality = "auto",
holidays = NULL, holidays = NULL,
seasonality.prior.scale = 10, seasonality.prior.scale = 10,
holidays.prior.scale = 10, holidays.prior.scale = 10,
changepoint.prior.scale = 0.05, changepoint.prior.scale = 0.05,
mcmc.samples = 0, mcmc.samples = 0,
interval.width = 0.8, interval.width = 0.8,
uncertainty.samples = 1000, uncertainty.samples = 1000,
fit = TRUE, fit = TRUE,
backend = NULL,
... ...
) )
} }
\arguments{ \arguments{
\item{df}{(optional) Dataframe containing the history. Must have columns ds \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 (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 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 provided, then the model object will be instantiated but not fit; use
fit.prophet(m, df) to fit the model.} fit.prophet(m, df) to fit the model.}
\item{growth}{String 'linear', 'logistic', or 'flat' to specify a linear, \item{growth}{String 'linear', 'logistic', or 'flat' to specify a linear,
logistic or flat trend.}
or flat trend.}
\item{changepoints}{Vector of dates at which to include potential \item{changepoints}{Vector of dates at which to include potential
changepoints. If not specified, potential changepoints are selected changepoints. If not specified, potential changepoints are selected
automatically.} automatically.}
\item{n.changepoints}{Number of potential changepoints to include. Not used \item{n.changepoints}{Number of potential changepoints to include. Not used
if input changepoints is supplied. If changepoints is not supplied, if input changepoints is supplied. If changepoints is not supplied,
then n.changepoints potential changepoints are selected uniformly from the then n.changepoints potential changepoints are selected uniformly from the
first changepoint.range proportion of df$ds.} first changepoint.range proportion of df$ds.}
\item{changepoint.range}{Proportion of history in which trend changepoints \item{changepoint.range}{Proportion of history in which trend changepoints
will be estimated. Defaults to 0.8 for the first 80%. Not used if will be estimated. Defaults to 0.8 for the first 80%. Not used if
changepoints is specified.} changepoints is specified.}
\item{yearly.seasonality}{Fit yearly seasonality. Can be 'auto', TRUE, \item{yearly.seasonality}{Fit yearly seasonality. Can be 'auto', TRUE, FALSE,
FALSE, or a number of Fourier terms to generate.} or a number of Fourier terms to generate.}
\item{weekly.seasonality}{Fit weekly seasonality. Can be 'auto', TRUE, \item{weekly.seasonality}{Fit weekly seasonality. Can be 'auto', TRUE, FALSE,
FALSE, or a number of Fourier terms to generate.} or a number of Fourier terms to generate.}
\item{daily.seasonality}{Fit daily seasonality. Can be 'auto', TRUE, \item{daily.seasonality}{Fit daily seasonality. Can be 'auto', TRUE, FALSE,
FALSE, or a number of Fourier terms to generate.} or a number of Fourier terms to generate.}
\item{holidays}{data frame with columns holiday (character) and ds (date \item{holidays}{data frame with columns holiday (character) and ds (date
type)and optionally columns lower_window and upper_window which specify a 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 range of days around the date to be included as holidays. lower_window=-2
will include 2 days prior to the date as holidays. Also optionally can have will include 2 days prior to the date as holidays. Also optionally can have
a column prior_scale specifying the prior scale for each holiday.} a column prior_scale specifying the prior scale for each holiday.}
\item{seasonality.prior.scale}{Parameter modulating the strength of the \item{seasonality.prior.scale}{Parameter modulating the strength of the
skipping to change at line 85 skipping to change at line 86
\item{changepoint.prior.scale}{Parameter modulating the flexibility of the \item{changepoint.prior.scale}{Parameter modulating the flexibility of the
automatic changepoint selection. Large values will allow many changepoints, automatic changepoint selection. Large values will allow many changepoints,
small values will allow few changepoints.} small values will allow few changepoints.}
\item{mcmc.samples}{Integer, if greater than 0, will do full Bayesian \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 inference with the specified number of MCMC samples. If 0, will do MAP
estimation.} estimation.}
\item{interval.width}{Numeric, width of the uncertainty intervals provided \item{interval.width}{Numeric, width of the uncertainty intervals provided
for the forecast. If mcmc.samples=0, this will be only the uncertainty for the forecast. If mcmc.samples=0, this will be only the uncertainty in
in the trend using the MAP estimate of the extrapolated generative model. the trend using the MAP estimate of the extrapolated generative model. If
If mcmc.samples>0, this will be integrated over all model parameters, mcmc.samples>0, this will be integrated over all model parameters, which
which will include uncertainty in seasonality.} will include uncertainty in seasonality.}
\item{uncertainty.samples}{Number of simulated draws used to estimate \item{uncertainty.samples}{Number of simulated draws used to estimate
uncertainty intervals. Settings this value to 0 or False will disable uncertainty intervals. Settings this value to 0 or False will disable
uncertainty estimation and speed up the calculation.} uncertainty estimation and speed up the calculation.}
\item{fit}{Boolean, if FALSE the model is initialized but not fit.} \item{fit}{Boolean, if FALSE the model is initialized but not fit.}
\item{backend}{Whether to use the "rstan" or "cmdstanr" backend to fit the
model. If not provided, uses the R_STAN_BACKEND environment variable.}