General Interface for PROPHET Time Series ModelsSource:
prophet_reg() is a way to generate a specification of a PROPHET model
before fitting and allows the model to be created using
different packages. Currently the only package is
prophet_reg( mode = "regression", growth = NULL, changepoint_num = NULL, changepoint_range = NULL, seasonality_yearly = NULL, seasonality_weekly = NULL, seasonality_daily = NULL, season = NULL, prior_scale_changepoints = NULL, prior_scale_seasonality = NULL, prior_scale_holidays = NULL, logistic_cap = NULL, logistic_floor = NULL )
A single character string for the type of model. The only possible value for this model is "regression".
String 'linear' or 'logistic' to specify a linear or logistic trend.
Number of potential changepoints to include for modeling trend.
Adjusts the flexibility of the trend component by limiting to a percentage of data before the end of the time series. 0.80 means that a changepoint cannot exist after the first 80% of the data.
One of "auto", TRUE or FALSE. Toggles on/off a seasonal component that models year-over-year seasonality.
One of "auto", TRUE or FALSE. Toggles on/off a seasonal component that models week-over-week seasonality.
One of "auto", TRUE or FALSE. Toggles on/off a seasonal componet that models day-over-day seasonality.
'additive' (default) or 'multiplicative'.
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 seasonality model. Larger values allow the model to fit larger seasonal fluctuations, smaller values dampen the seasonality.
Parameter modulating the strength of the holiday components model, unless overridden in the holidays input.
When growth is logistic, the upper-bound for "saturation".
When growth is logistic, the lower-bound for "saturation".
The data given to the function are not saved and are only used
to determine the mode of the model. For
mode will always be "regression".
The model can be created using the
fit() function using the
"prophet" (default) - Connects to
The main arguments (tuning parameters) for the model are:
growth: String 'linear' or 'logistic' to specify a linear or logistic trend.
changepoint_num: Number of potential changepoints to include for modeling trend.
changepoint_range: Range changepoints that adjusts how close to the end the last changepoint can be located.
season: 'additive' (default) or 'multiplicative'.
prior_scale_changepoints: Parameter modulating the flexibility of the automatic changepoint selection. Large values will allow many changepoints, small values will allow few changepoints.
prior_scale_seasonality: Parameter modulating the strength of the seasonality model. Larger values allow the model to fit larger seasonal fluctuations, smaller values dampen the seasonality.
prior_scale_holidays: Parameter modulating the strength of the holiday components model, unless overridden in the holidays input.
logistic_cap: When growth is logistic, the upper-bound for "saturation".
logistic_floor: When growth is logistic, the lower-bound for "saturation".
These arguments are converted to their specific names at the time that the model is fit.
Other options and argument can be
set_engine() (See Engine Details below).
If parameters need to be modified,
update() can be used
in lieu of recreating the object from scratch.
The standardized parameter names in
modeltime can be mapped to their original
names in each engine:
Other options can be set using
The engine uses
#> function (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, #> mcmc.samples = 0, interval.width = 0.8, uncertainty.samples = 1000, #> fit = TRUE, ...)
df: This is supplied via the parsnip / modeltime
fit()interface (so don't provide this manually). See Fit Details (below).
holidays: A data.frame of holidays can be supplied via
uncertainty.samples: The default is set to 0 because the prophet uncertainty intervals are not used as part of the Modeltime Workflow. You can override this setting if you plan to use prophet's uncertainty tools.
Regressors are provided via the
recipesinterface, which passes regressors to
Parameters can be controlled in
The regressor prior scale implementation default is
regressors.prior.scale = 1e4, which deviates from the
prophetimplementation (defaults to holidays.prior.scale)
Logistic Growth and Saturation Levels:
growth = "logistic", simply add numeric values for
logistic_capand / or
logistic_floor. There is no need to add additional columns for "cap" and "floor" to your data frame.
prophet::add_seasonality()is not currently implemented. It's used to specify non-standard seasonalities using fourier series. An alternative is to use
step_fourier()and supply custom seasonalities as Extra Regressors.
Date and Date-Time Variable
It's a requirement to have a date or date-time variable as a predictor.
fit() interface accepts date and date-time features and handles them internally.
fit(y ~ date)
Univariate (No Extra Regressors):
For univariate analysis, you must include a date or date-time feature. Simply use:
Formula Interface (recommended):
fit(y ~ date)will ignore xreg's.
fit_xy(x = data[,"date"], y = data$y)will ignore xreg's.
Multivariate (Extra Regressors)
Extra Regressors parameter is populated using the
ordered factor, and
numericdata will be used as xregs.
Date and Date-time variables are not used as xregs
characterdata should be converted to factor.
Xreg Example: Suppose you have 3 features:
month.lbl(labeled month as a ordered factor).
month.lbl is an exogenous regressor that can be passed to the
fit(y ~ date + month.lbl)will pass
month.lblon as an exogenous regressor.
fit_xy(data[,c("date", "month.lbl")], y = data$y)will pass x, where x is a data frame containing
month.lblwill be used as an exogenous regressor.
Note that date or date-time class values are excluded from
library(dplyr) library(parsnip) library(rsample) library(timetk) library(modeltime) # Data m750 <- m4_monthly %>% filter(id == "M750") m750 #> # A tibble: 306 × 3 #> id date value #> <fct> <date> <dbl> #> 1 M750 1990-01-01 6370 #> 2 M750 1990-02-01 6430 #> 3 M750 1990-03-01 6520 #> 4 M750 1990-04-01 6580 #> 5 M750 1990-05-01 6620 #> 6 M750 1990-06-01 6690 #> 7 M750 1990-07-01 6000 #> 8 M750 1990-08-01 5450 #> 9 M750 1990-09-01 6480 #> 10 M750 1990-10-01 6820 #> # … with 296 more rows # Split Data 80/20 splits <- initial_time_split(m750, prop = 0.8) # ---- PROPHET ---- # Model Spec model_spec <- prophet_reg() %>% set_engine("prophet") # Fit Spec model_fit <- model_spec %>% fit(log(value) ~ date, data = training(splits)) #> Disabling weekly seasonality. Run prophet with weekly.seasonality=TRUE to override this. #> Disabling daily seasonality. Run prophet with daily.seasonality=TRUE to override this. model_fit #> parsnip model object #> #> PROPHET Model #> - growth: 'linear' #> - n.changepoints: 25 #> - changepoint.range: 0.8 #> - yearly.seasonality: 'auto' #> - weekly.seasonality: 'auto' #> - daily.seasonality: 'auto' #> - seasonality.mode: 'additive' #> - changepoint.prior.scale: 0.05 #> - seasonality.prior.scale: 10 #> - holidays.prior.scale: 10 #> - logistic_cap: NULL #> - logistic_floor: NULL #> - extra_regressors: 0