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.