# General Interface for Boosted PROPHET Time Series Models

Source:`R/parsnip-prophet_boost.R`

`prophet_boost.Rd`

`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`

.

## Usage

```
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 (specific engines 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) (specific engines only).

- learn_rate
A number for the rate at which the boosting algorithm adapts from iteration-to-iteration (specific engines only). This is sometimes referred to as the shrinkage parameter.

- loss_reduction
A number for the reduction in the loss function required to split further (specific engines 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*:

"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 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:

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`

):

```
#> 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.

`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.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 Date-time 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 date-time class values are excluded from `xreg`

.

## Examples

```
library(dplyr)
library(lubridate)
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_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
}
```