prophet.Rd (prophet-1.0) | : | prophet.Rd (prophet-1.1) | ||
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skipping to change at line 25 | skipping to change at line 25 | |||

daily.seasonality = "auto", | daily.seasonality = "auto", | |||

holidays = NULL, | holidays = NULL, | |||

seasonality.mode = "additive", | seasonality.mode = "additive", | |||

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, logist | \item{growth}{String 'linear', 'logistic', or 'flat' to specify a linear, | |||

ic | 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.mode}{'additive' (default) or 'multiplicative'.} | \item{seasonality.mode}{'additive' (default) or 'multiplicative'.} | |||

\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.} | ||||

\item{...}{Additional arguments, passed to \code{\link{fit.prophet}}} | \item{...}{Additional arguments, passed to \code{\link{fit.prophet}}} | |||

} | } | |||

\value{ | \value{ | |||

A prophet model. | A prophet model. | |||

} | } | |||

\description{ | \description{ | |||

Prophet forecaster. | Prophet forecaster. | |||

} | } | |||

\examples{ | \examples{ | |||

\dontrun{ | \dontrun{ | |||

End of changes. 7 change blocks. | ||||

13 lines changed or deleted | | 16 lines changed or added |