prophet
From prophet v0.1
by Sean Taylor
Prophet forecast.
Prophet forecast.
Usage
prophet(df = df, growth = "linear", changepoints = NULL, n.changepoints = 25, yearly.seasonality = TRUE, weekly.seasonality = TRUE, holidays = NULL, seasonality.prior.scale = 10, changepoint.prior.scale = 0.05, holidays.prior.scale = 10, mcmc.samples = 0, interval.width = 0.8, uncertainty.samples = 1000, fit = TRUE)
Arguments
 df
 Data frame with 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.
 growth
 String 'linear' or 'logistic' to specify a linear or logistic trend.
 changepoints
 Vector of dates at which to include potential changepoints. Each date must be present in df$ds. If not specified, potential changepoints are selected automatically.
 n.changepoints
 Number of potential changepoints to include. Not used if input `changepoints` is supplied. If `changepoints` is not supplied, then n.changepoints potential changepoints are selected uniformly from the first 80 percent of df$ds.
 yearly.seasonality
 Boolean, fit yearly seasonality.
 weekly.seasonality
 Boolean, fit weekly seasonality.
 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.
 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.
 changepoint.prior.scale
 Parameter modulating the flexibility of the automatic changepoint selection. Large values will allow many changepoints, small values will allow few changepoints.
 holidays.prior.scale
 Parameter modulating the strength of the holiday components model.
 mcmc.samples
 Integer, if great than 0, will do full Bayesian inference with the specified number of MCMC samples. If 0, will do MAP estimation.
 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.
 uncertainty.samples
 Number of simulated draws used to estimate uncertainty intervals.
 fit
 Boolean, if FALSE the model is initialized but not fit.
Value

A prophet model.
Examples
## Not run:
# history < data.frame(ds = seq(as.Date('20150101'), as.Date('20160101'), by = 'd'),
# y = sin(1:366/200) + rnorm(366)/10)
# m < prophet(history)
# ## End(Not run)
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