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81 lines
2.9 KiB
R
81 lines
2.9 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 forecast.}
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\usage{
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prophet(df = df, growth = "linear", changepoints = NULL,
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n.changepoints = 25, yearly.seasonality = TRUE,
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weekly.seasonality = TRUE, holidays = NULL,
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seasonality.prior.scale = 10, changepoint.prior.scale = 0.05,
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holidays.prior.scale = 10, mcmc.samples = 0, interval.width = 0.8,
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uncertainty.samples = 1000, fit = TRUE)
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}
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\arguments{
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\item{df}{Data frame with columns ds (date type) and y, the time series.
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If growth is logistic, then df must also have a column cap that specifies
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the capacity at each ds.}
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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. Each date must be present in df$ds. If not specified,
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potential changepoints are selected automatically.}
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\item{n.changepoints}{Number of potential changepoints to include. Not used
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if input `changepoints` is supplied. If `changepoints` is not supplied,
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then n.changepoints potential changepoints are selected uniformly from the
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first 80 percent of df$ds.}
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\item{yearly.seasonality}{Boolean, fit yearly seasonality.}
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\item{weekly.seasonality}{Boolean, fit weekly seasonality.}
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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.}
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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.}
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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{holidays.prior.scale}{Parameter modulating the strength of the holiday
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components model.}
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\item{mcmc.samples}{Integer, if great 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 forecast.
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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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