mirror of
https://github.com/saymrwulf/prophet.git
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95fa4460a9
* Allow both both hoidays and append holidays Match holidays in predict and fit Add test for append_holiday features; minor fixes Add column name validation for append_holidays names; allow only one country Fix bug about holiday year different from input year and also other bugs Change function description Add append holiday feature for R * Add test for R/Pyrhon; fix bugs
100 lines
4 KiB
R
100 lines
4 KiB
R
% Generated by roxygen2: do not edit by hand
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% Please edit documentation in R/prophet.R
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\name{prophet}
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\alias{prophet}
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\title{Prophet forecaster.}
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\usage{
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prophet(df = NULL, growth = "linear", changepoints = NULL,
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n.changepoints = 25, changepoint.range = 0.8,
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yearly.seasonality = "auto", weekly.seasonality = "auto",
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daily.seasonality = "auto", holidays = NULL,
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append.holidays = NULL, seasonality.mode = "additive",
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seasonality.prior.scale = 10, holidays.prior.scale = 10,
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changepoint.prior.scale = 0.05, mcmc.samples = 0,
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interval.width = 0.8, uncertainty.samples = 1000, fit = TRUE, ...)
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}
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\arguments{
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\item{df}{(optional) Dataframe containing the history. Must have columns ds
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(date type) and y, the time series. If growth is logistic, then df must
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also have a column cap that specifies the capacity at each ds. If not
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provided, then the model object will be instantiated but not fit; use
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fit.prophet(m, df) to fit the model.}
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\item{growth}{String 'linear' or 'logistic' to specify a linear or logistic
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trend.}
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\item{changepoints}{Vector of dates at which to include potential
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changepoints. If not specified, potential changepoints are selected
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automatically.}
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\item{n.changepoints}{Number of potential changepoints to include. Not used
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if input `changepoints` is supplied. If `changepoints` is not supplied,
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then n.changepoints potential changepoints are selected uniformly from the
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first `changepoint.range` proportion of df$ds.}
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\item{changepoint.range}{Proportion of history in which trend changepoints
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will be estimated. Defaults to 0.8 for the first 80%. Not used if
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`changepoints` is specified.}
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\item{yearly.seasonality}{Fit yearly seasonality. Can be 'auto', TRUE,
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FALSE, or a number of Fourier terms to generate.}
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\item{weekly.seasonality}{Fit weekly seasonality. Can be 'auto', TRUE,
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FALSE, or a number of Fourier terms to generate.}
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\item{daily.seasonality}{Fit daily seasonality. Can be 'auto', TRUE,
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FALSE, or a number of Fourier terms to generate.}
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\item{holidays}{data frame with columns holiday (character) and ds (date
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type)and optionally columns lower_window and upper_window which specify a
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range of days around the date to be included as holidays. lower_window=-2
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will include 2 days prior to the date as holidays. Also optionally can have
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a column prior_scale specifying the prior scale for each holiday.}
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\item{append.holidays}{country name or abbreviation (character).}
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\item{seasonality.mode}{'additive' (default) or 'multiplicative'.}
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\item{seasonality.prior.scale}{Parameter modulating the strength of the
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seasonality model. Larger values allow the model to fit larger seasonal
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fluctuations, smaller values dampen the seasonality. Can be specified for
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individual seasonalities using add_seasonality.}
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\item{holidays.prior.scale}{Parameter modulating the strength of the holiday
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components model, unless overridden in the holidays input.}
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\item{changepoint.prior.scale}{Parameter modulating the flexibility of the
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automatic changepoint selection. Large values will allow many changepoints,
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small values will allow few changepoints.}
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\item{mcmc.samples}{Integer, if greater than 0, will do full Bayesian
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inference with the specified number of MCMC samples. If 0, will do MAP
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estimation.}
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\item{interval.width}{Numeric, width of the uncertainty intervals provided
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for the forecast. If mcmc.samples=0, this will be only the uncertainty
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in the trend using the MAP estimate of the extrapolated generative model.
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If mcmc.samples>0, this will be integrated over all model parameters,
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which will include uncertainty in seasonality.}
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\item{uncertainty.samples}{Number of simulated draws used to estimate
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uncertainty intervals.}
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\item{fit}{Boolean, if FALSE the model is initialized but not fit.}
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\item{...}{Additional arguments, passed to \code{\link{fit.prophet}}}
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}
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\value{
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A prophet model.
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}
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\description{
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Prophet forecaster.
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}
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\examples{
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\dontrun{
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history <- data.frame(ds = seq(as.Date('2015-01-01'), as.Date('2016-01-01'), by = 'd'),
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y = sin(1:366/200) + rnorm(366)/10)
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m <- prophet(history)
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}
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}
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