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)
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.
String 'linear' or 'logistic' to specify a linear or logistic
Vector of dates at which to include potential
changepoints. Each date must be present in df$ds. If not specified,
potential changepoints are selected automatically.
Number of potential changepoints to include. Not used
if input `changepoints` is supplied. If `changepoints` is not supplied,
then n.changepoints potential changepoints are selected uniformly from the
first 80 percent of df$ds.
Boolean, fit yearly seasonality.
Boolean, fit weekly seasonality.
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.
Parameter modulating the strength of the
seasonality model. Larger values allow the model to fit larger seasonal
fluctuations, smaller values dampen the seasonality.
Parameter modulating the flexibility of the
automatic changepoint selection. Large values will allow many changepoints,
small values will allow few changepoints.
Parameter modulating the strength of the holiday
Integer, if great than 0, will do full Bayesian
inference with the specified number of MCMC samples. If 0, will do MAP
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.
Number of simulated draws used to estimate
Boolean, if FALSE the model is initialized but not fit.