is a tool for producing high quality forecasts for time series data that has multiple seasonality with linear or non-linear growth.
| prophet.Rd (prophet-1.0) | : | prophet.Rd (prophet-1.1) |
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| | |
| skipping to change at line 25 | | skipping to change at line 25 |
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| daily.seasonality = "auto", | | daily.seasonality = "auto", |
| holidays = NULL, | | holidays = NULL, |
| seasonality.mode = "additive", | | seasonality.mode = "additive", |
| seasonality.prior.scale = 10, | | seasonality.prior.scale = 10, |
| holidays.prior.scale = 10, | | holidays.prior.scale = 10, |
| changepoint.prior.scale = 0.05, | | changepoint.prior.scale = 0.05, |
| mcmc.samples = 0, | | mcmc.samples = 0, |
| interval.width = 0.8, | | interval.width = 0.8, |
| uncertainty.samples = 1000, | | uncertainty.samples = 1000, |
| fit = TRUE, | | fit = TRUE, |
|
| | backend = NULL, |
| ... | | ... |
| ) | | ) |
| } | | } |
| \arguments{ | | \arguments{ |
| \item{df}{(optional) Dataframe containing the history. Must have columns ds | | \item{df}{(optional) Dataframe containing the history. Must have columns ds |
| (date type) and y, the time series. If growth is logistic, then df must | | (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. If not | | also have a column cap that specifies the capacity at each ds. If not |
| provided, then the model object will be instantiated but not fit; use | | provided, then the model object will be instantiated but not fit; use |
| fit.prophet(m, df) to fit the model.} | | fit.prophet(m, df) to fit the model.} |
| | |
|
| \item{growth}{String 'linear', 'logistic', or 'flat' to specify a linear, logist | | \item{growth}{String 'linear', 'logistic', or 'flat' to specify a linear, |
| ic | | logistic or flat trend.} |
| or flat trend.} | | |
| | |
| \item{changepoints}{Vector of dates at which to include potential | | \item{changepoints}{Vector of dates at which to include potential |
| changepoints. If not specified, potential changepoints are selected | | changepoints. If not specified, potential changepoints are selected |
| automatically.} | | automatically.} |
| | |
| \item{n.changepoints}{Number of potential changepoints to include. Not used | | \item{n.changepoints}{Number of potential changepoints to include. Not used |
| if input `changepoints` is supplied. If `changepoints` is not supplied, | | if input `changepoints` is supplied. If `changepoints` is not supplied, |
| then n.changepoints potential changepoints are selected uniformly from the | | then n.changepoints potential changepoints are selected uniformly from the |
| first `changepoint.range` proportion of df$ds.} | | first `changepoint.range` proportion of df$ds.} |
| | |
| \item{changepoint.range}{Proportion of history in which trend changepoints | | \item{changepoint.range}{Proportion of history in which trend changepoints |
| will be estimated. Defaults to 0.8 for the first 80%. Not used if | | will be estimated. Defaults to 0.8 for the first 80%. Not used if |
| `changepoints` is specified.} | | `changepoints` is specified.} |
| | |
|
| \item{yearly.seasonality}{Fit yearly seasonality. Can be 'auto', TRUE, | | \item{yearly.seasonality}{Fit yearly seasonality. Can be 'auto', TRUE, FALSE, |
| FALSE, or a number of Fourier terms to generate.} | | or a number of Fourier terms to generate.} |
| | |
|
| \item{weekly.seasonality}{Fit weekly seasonality. Can be 'auto', TRUE, | | \item{weekly.seasonality}{Fit weekly seasonality. Can be 'auto', TRUE, FALSE, |
| FALSE, or a number of Fourier terms to generate.} | | or a number of Fourier terms to generate.} |
| | |
|
| \item{daily.seasonality}{Fit daily seasonality. Can be 'auto', TRUE, | | \item{daily.seasonality}{Fit daily seasonality. Can be 'auto', TRUE, FALSE, |
| FALSE, or a number of Fourier terms to generate.} | | or a number of Fourier terms to generate.} |
| | |
| \item{holidays}{data frame with columns holiday (character) and ds (date | | \item{holidays}{data frame with columns holiday (character) and ds (date |
| type)and optionally columns lower_window and upper_window which specify a | | type)and optionally columns lower_window and upper_window which specify a |
| range of days around the date to be included as holidays. lower_window=-2 | | range of days around the date to be included as holidays. lower_window=-2 |
| will include 2 days prior to the date as holidays. Also optionally can have | | will include 2 days prior to the date as holidays. Also optionally can have |
| a column prior_scale specifying the prior scale for each holiday.} | | a column prior_scale specifying the prior scale for each holiday.} |
| | |
| \item{seasonality.mode}{'additive' (default) or 'multiplicative'.} | | \item{seasonality.mode}{'additive' (default) or 'multiplicative'.} |
| | |
| \item{seasonality.prior.scale}{Parameter modulating the strength of the | | \item{seasonality.prior.scale}{Parameter modulating the strength of the |
| | |
| skipping to change at line 85 | | skipping to change at line 86 |
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| \item{changepoint.prior.scale}{Parameter modulating the flexibility of the | | \item{changepoint.prior.scale}{Parameter modulating the flexibility of the |
| automatic changepoint selection. Large values will allow many changepoints, | | automatic changepoint selection. Large values will allow many changepoints, |
| small values will allow few changepoints.} | | small values will allow few changepoints.} |
| | |
| \item{mcmc.samples}{Integer, if greater than 0, will do full Bayesian | | \item{mcmc.samples}{Integer, if greater than 0, will do full Bayesian |
| inference with the specified number of MCMC samples. If 0, will do MAP | | inference with the specified number of MCMC samples. If 0, will do MAP |
| estimation.} | | estimation.} |
| | |
| \item{interval.width}{Numeric, width of the uncertainty intervals provided | | \item{interval.width}{Numeric, width of the uncertainty intervals provided |
|
| for the forecast. If mcmc.samples=0, this will be only the uncertainty | | for the forecast. If mcmc.samples=0, this will be only the uncertainty in |
| in the trend using the MAP estimate of the extrapolated generative model. | | the trend using the MAP estimate of the extrapolated generative model. If |
| If mcmc.samples>0, this will be integrated over all model parameters, | | mcmc.samples>0, this will be integrated over all model parameters, which |
| which will include uncertainty in seasonality.} | | will include uncertainty in seasonality.} |
| | |
| \item{uncertainty.samples}{Number of simulated draws used to estimate | | \item{uncertainty.samples}{Number of simulated draws used to estimate |
| uncertainty intervals. Settings this value to 0 or False will disable | | uncertainty intervals. Settings this value to 0 or False will disable |
| uncertainty estimation and speed up the calculation.} | | uncertainty estimation and speed up the calculation.} |
| | |
| \item{fit}{Boolean, if FALSE the model is initialized but not fit.} | | \item{fit}{Boolean, if FALSE the model is initialized but not fit.} |
| | |
|
| | \item{backend}{Whether to use the "rstan" or "cmdstanr" backend to fit the |
| | model. If not provided, uses the R_STAN_BACKEND environment variable.} |
| | |
| \item{...}{Additional arguments, passed to \code{\link{fit.prophet}}} | | \item{...}{Additional arguments, passed to \code{\link{fit.prophet}}} |
| } | | } |
| \value{ | | \value{ |
| A prophet model. | | A prophet model. |
| } | | } |
| \description{ | | \description{ |
| Prophet forecaster. | | Prophet forecaster. |
| } | | } |
| \examples{ | | \examples{ |
| \dontrun{ | | \dontrun{ |
| | | |
| End of changes. 7 change blocks. |
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| 13 lines changed or deleted | | 16 lines changed or added |
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"Fossies" - the Fresh Open Source Software Archive 