prophet_boost() is a way to generate a specification of a Boosted PROPHET model before fitting and allows the model to be created using different packages. Currently the only package is prophet.

prophet_boost(
  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,
  mtry = NULL,
  trees = NULL,
  min_n = NULL,
  tree_depth = NULL,
  learn_rate = NULL,
  loss_reduction = NULL,
  sample_size = NULL,
  stop_iter = NULL
)

Arguments

mode

A single character string for the type of model. The only possible value for this model is "regression".

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

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.

seasonality_yearly

One of "auto", TRUE or FALSE. Toggles on/off a seasonal component that models year-over-year seasonality.

seasonality_weekly

One of "auto", TRUE or FALSE. Toggles on/off a seasonal component that models week-over-week seasonality.

seasonality_daily

One of "auto", TRUE or FALSE. Toggles on/off a seasonal componet that models day-over-day seasonality.

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".

mtry

A number for the number (or proportion) of predictors that will be randomly sampled at each split when creating the tree models (xgboost only).

trees

An integer for the number of trees contained in the ensemble.

min_n

An integer for the minimum number of data points in a node that is required for the node to be split further.

tree_depth

An integer for the maximum depth of the tree (i.e. number of splits) (xgboost only).

learn_rate

A number for the rate at which the boosting algorithm adapts from iteration-to-iteration (xgboost only).

loss_reduction

A number for the reduction in the loss function required to split further (xgboost only).

sample_size

number for the number (or proportion) of data that is exposed to the fitting routine.

stop_iter

The number of iterations without improvement before stopping (xgboost only).

Details

The data given to the function are not saved and are only used to determine the mode of the model. For prophet_boost(), the mode will always be "regression".

The model can be created using the fit() function using the following engines:

Main Arguments

The main arguments (tuning parameters) for the PROPHET 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".

The main arguments (tuning parameters) for the model XGBoost model are:

  • mtry: The number of predictors that will be randomly sampled at each split when creating the tree models.

  • trees: The number of trees contained in the ensemble.

  • min_n: The minimum number of data points in a node that are required for the node to be split further.

  • tree_depth: The maximum depth of the tree (i.e. number of splits).

  • learn_rate: The rate at which the boosting algorithm adapts from iteration-to-iteration.

  • loss_reduction: The reduction in the loss function required to split further.

  • sample_size: The amount of data exposed to the fitting routine.

  • stop_iter: The number of iterations without improvement before stopping.

These arguments are converted to their specific names at the time that the model is fit.

Other options and argument can be set using set_engine() (See Engine Details below).

If parameters need to be modified, update() can be used in lieu of recreating the object from scratch.

Engine Details

The standardized parameter names in modeltime can be mapped to their original names in each engine:

Model 1: PROPHET:

modeltimeprophet
growthgrowth ('linear')
changepoint_numn.changepoints (25)
changepoint_rangechangepoints.range (0.8)
seasonality_yearlyyearly.seasonality ('auto')
seasonality_weeklyweekly.seasonality ('auto')
seasonality_dailydaily.seasonality ('auto')
seasonseasonality.mode ('additive')
prior_scale_changepointschangepoint.prior.scale (0.05)
prior_scale_seasonalityseasonality.prior.scale (10)
prior_scale_holidaysholidays.prior.scale (10)
logistic_capdf$cap (NULL)
logistic_floordf$floor (NULL)

Model 2: XGBoost:

modeltimexgboost::xgb.train
tree_depthmax_depth (6)
treesnrounds (15)
learn_rateeta (0.3)
mtrycolsample_bytree (1)
min_nmin_child_weight (1)
loss_reductiongamma (0)
sample_sizesubsample (1)
stop_iterearly_stop

Other options can be set using set_engine().

prophet_xgboost

Model 1: PROPHET (prophet::prophet):

## 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, ...)

Parameter Notes:

  • 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 set_engine()

  • 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.

Logistic Growth and Saturation Levels:

  • For growth = "logistic", simply add numeric values for logistic_cap and / or logistic_floor. There is no need to add additional columns for "cap" and "floor" to your data frame.

Limitations:

  • 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.

Model 2: XGBoost (xgboost::xgb.train):

## function (params = list(), data, nrounds, watchlist = list(), obj = NULL, 
##     feval = NULL, verbose = 1, print_every_n = 1L, early_stopping_rounds = NULL, 
##     maximize = NULL, save_period = NULL, save_name = "xgboost.model", xgb_model = NULL, 
##     callbacks = list(), ...)

Parameter Notes:

  • XGBoost uses a params = list() to capture. Parsnip / Modeltime automatically sends any args provided as ... inside of set_engine() to the params = list(...).

Fit Details

Date and Date-Time Variable

It's a requirement to have a date or date-time variable as a predictor. The fit() interface accepts date and date-time features and handles them internally.

Univariate (No Extra Regressors):

For univariate analysis, you must include a date or date-time feature. Simply use:

Multivariate (Extra Regressors)

Extra Regressors parameter is populated using the fit() or fit_xy() function:

  • Only factor, ordered factor, and numeric data will be used as xregs.

  • Date and Date-time variables are not used as xregs

  • character data should be converted to factor.

Xreg Example: Suppose you have 3 features:

  1. y (target)

  2. date (time stamp),

  3. month.lbl (labeled month as a ordered factor).

The month.lbl is an exogenous regressor that can be passed to the arima_reg() using fit():

Note that date or date-time class values are excluded from xreg.

See also

Examples

library(dplyr) library(lubridate) library(parsnip) library(rsample) library(timetk) library(modeltime) # Data m750 <- m4_monthly %>% filter(id == "M750") m750
#> # A tibble: 306 x 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_boost( learn_rate = 0.1 ) %>% set_engine("prophet_xgboost") # Fit Spec if (FALSE) { model_fit <- model_spec %>% fit(log(value) ~ date + as.numeric(date) + month(date, label = TRUE), data = training(splits)) model_fit }