Description Usage Arguments Details Engine Details Fit Details See Also Examples
View source: R/parsnipprophet_boost.R
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
.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23  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
)

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 yearoveryear seasonality. 
seasonality_weekly 
One of "auto", TRUE or FALSE. Toggles on/off a seasonal component that models weekoverweek seasonality. 
seasonality_daily 
One of "auto", TRUE or FALSE. Toggles on/off a seasonal componet that models dayoverday 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 upperbound for "saturation". 
logistic_floor 
When growth is logistic, the lowerbound 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 ( 
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) ( 
learn_rate 
A number for the rate at which the boosting algorithm adapts
from iterationtoiteration ( 
loss_reduction 
A number for the reduction in the loss function required
to split further ( 
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 ( 
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:
"prophet_xgboost" (default)  Connects to prophet::prophet()
and xgboost::xgb.train()
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 upperbound for "saturation".
logistic_floor
: When growth is logistic, the lowerbound 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 iterationtoiteration.
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.
The standardized parameter names in modeltime
can be mapped to their original
names in each engine:
Model 1: PROPHET:
modeltime  prophet 
growth  growth ('linear') 
changepoint_num  n.changepoints (25) 
changepoint_range  changepoints.range (0.8) 
seasonality_yearly  yearly.seasonality ('auto') 
seasonality_weekly  weekly.seasonality ('auto') 
seasonality_daily  daily.seasonality ('auto') 
season  seasonality.mode ('additive') 
prior_scale_changepoints  changepoint.prior.scale (0.05) 
prior_scale_seasonality  seasonality.prior.scale (10) 
prior_scale_holidays  holidays.prior.scale (10) 
logistic_cap  df$cap (NULL) 
logistic_floor  df$floor (NULL) 
Model 2: XGBoost:
modeltime  xgboost::xgb.train 
tree_depth  max_depth (6) 
trees  nrounds (15) 
learn_rate  eta (0.3) 
mtry  colsample_bynode (1) 
min_n  min_child_weight (1) 
loss_reduction  gamma (0) 
sample_size  subsample (1) 
stop_iter  early_stop 
Other options can be set using set_engine()
.
prophet_xgboost
Model 1: PROPHET (prophet::prophet
):
1 2 3 4 5 6  ## 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 nonstandard seasonalities using fourier series. An alternative is to use
step_fourier()
and supply custom seasonalities as Extra Regressors.
Model 2: XGBoost (xgboost::xgb.train
):
1 2 3 4  ## 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(...)
.
Date and DateTime Variable
It's a requirement to have a date or datetime variable as a predictor.
The fit()
interface accepts date and datetime features and handles them internally.
fit(y ~ date)
Univariate (No Extra Regressors):
For univariate analysis, you must include a date or datetime feature. Simply use:
Formula Interface (recommended): fit(y ~ date)
will ignore xreg's.
XY Interface: fit_xy(x = data[,"date"], y = data$y)
will ignore xreg's.
Multivariate (Extra Regressors)
Extra Regressors parameter is populated using the fit()
or fit_xy()
function:
Only factor
, ordered factor
, and numeric
data will be used as xregs.
Date and Datetime variables are not used as xregs
character
data should be converted to factor.
Xreg Example: Suppose you have 3 features:
y
(target)
date
(time stamp),
month.lbl
(labeled month as a ordered factor).
The month.lbl
is an exogenous regressor that can be passed to the arima_reg()
using
fit()
:
fit(y ~ date + month.lbl)
will pass month.lbl
on as an exogenous regressor.
fit_xy(data[,c("date", "month.lbl")], y = data$y)
will pass x, where x is a data frame containing month.lbl
and the date
feature. Only month.lbl
will be used as an exogenous regressor.
Note that date or datetime class values are excluded from xreg
.
fit.model_spec()
, set_engine()
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30  library(dplyr)
library(lubridate)
library(parsnip)
library(rsample)
library(timetk)
library(modeltime)
# Data
m750 < m4_monthly %>% filter(id == "M750")
m750
# 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
## Not run:
model_fit < model_spec %>%
fit(log(value) ~ date + as.numeric(date) + month(date, label = TRUE),
data = training(splits))
model_fit
## End(Not run)

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