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 trend.
- 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 components model.
- Integer, if great than 0, will do full Bayesian inference with the specified number of MCMC samples. If 0, will do MAP estimation.
- 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 uncertainty intervals.
- Boolean, if FALSE the model is initialized but not fit.
A prophet model.
## Not run: # history <- data.frame(ds = seq(as.Date('2015-01-01'), as.Date('2016-01-01'), by = 'd'), # y = sin(1:366/200) + rnorm(366)/10) # m <- prophet(history) # ## End(Not run)
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