prophet_catboost_fit_impl: Bridge Prophet-Catboost Modeling function

In AlbertoAlmuinha/boostime: The Tidymodels Extension for Time Series Boosting Models

Bridge Prophet-Catboost Modeling function

Description

Bridge Prophet-Catboost Modeling function

Usage

prophet_catboost_fit_impl(
  x,
  y,
  df = NULL,
  growth = "linear",
  changepoints = NULL,
  n.changepoints = 25,
  changepoint.range = 0.8,
  yearly.seasonality = "auto",
  weekly.seasonality = "auto",
  daily.seasonality = "auto",
  holidays = NULL,
  seasonality.mode = "additive",
  seasonality.prior.scale = 10,
  holidays.prior.scale = 10,
  changepoint.prior.scale = 0.05,
  logistic_cap = NULL,
  logistic_floor = NULL,
  mcmc.samples = 0,
  interval.width = 0.8,
  uncertainty.samples = 1000,
  fit = TRUE,
  depth = 6,
  eta = 0.3,
  rsm = 1,
  iterations = 1000,
  min_data_in_leaf = 1,
  subsample = 1,
  ...
)

Arguments

x	A dataframe of xreg (exogenous regressors)
y	A numeric vector of values to fit
df	(optional) Dataframe containing the history. Must have 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. If not provided, then the model object will be instantiated but not fit; use fit.prophet(m, df) to fit the model.
growth	String 'linear', 'logistic', or 'flat' to specify a linear, logistic or flat trend.
changepoints	Vector of dates at which to include potential changepoints. 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 'changepoint.range' proportion of df$ds.
changepoint.range	Proportion of history in which trend changepoints will be estimated. Defaults to 0.8 for the first 80 'changepoints' is specified.
yearly.seasonality	Fit yearly seasonality. Can be 'auto', TRUE, FALSE, or a number of Fourier terms to generate.
weekly.seasonality	Fit weekly seasonality. Can be 'auto', TRUE, FALSE, or a number of Fourier terms to generate.
daily.seasonality	Fit daily seasonality. Can be 'auto', TRUE, FALSE, or a number of Fourier terms to generate.
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. lower_window=-2 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.
seasonality.mode	'additive' (default) or 'multiplicative'.
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. Can be specified for individual seasonalities using add_seasonality.
holidays.prior.scale	Parameter modulating the strength of the holiday components model, unless overridden in the holidays input.
changepoint.prior.scale	Parameter modulating the flexibility of the automatic changepoint selection. Large values will allow many changepoints, small values will allow few changepoints.
logistic_cap	When growth is logistic, the upper-bound for "saturation".
logistic_floor	When growth is logistic, the lower-bound for "saturation".
mcmc.samples	Integer, if greater 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. Settings this value to 0 or False will disable uncertainty estimation and speed up the calculation.
fit	Boolean, if FALSE the model is initialized but not fit.
depth	The maximum depth of the tree (i.e. number of splits).
eta	The rate at which the boosting algorithm adapts from iteration-to-iteration.
rsm	The number of predictors that will be randomly sampled at each split when creating the tree models.
iterations	The number of trees contained in the ensemble.
min_data_in_leaf	The minimum number of data points in a node that is required for the node to be split further.
subsample	The amount of data exposed to the fitting routine.
...	Additional arguments passed to catboost::catboost.train

AlbertoAlmuinha/boostime documentation built on Aug. 13, 2022, 1:46 p.m.