View source: R/wfs-nnetar-reg.R
ts_wfs_nnetar_reg | R Documentation |
This function is used to quickly create a workflowsets object.
ts_wfs_nnetar_reg(
.model_type = "nnetar",
.recipe_list,
.non_seasonal_ar = 0,
.seasonal_ar = 0,
.hidden_units = 5,
.num_networks = 10,
.penalty = 0.1,
.epochs = 10
)
.model_type |
This is where you will set your engine. It uses
|
.recipe_list |
You must supply a list of recipes. list(rec_1, rec_2, ...) |
.non_seasonal_ar |
The order of the non-seasonal auto-regressive (AR) terms. Often denoted "p" in pdq-notation. |
.seasonal_ar |
The order of the seasonal auto-regressive (SAR) terms. Often denoted "P" in PDQ-notation. |
.hidden_units |
An integer for the number of units in the hidden model. |
.num_networks |
Number of networks to fit with different random starting weights. These are then averaged when producing forecasts. |
.penalty |
A non-negative numeric value for the amount of weight decay. |
.epochs |
An integer for the number of training iterations. |
This function expects to take in the recipes that you want to use in the modeling process. This is an automated workflow process. There are sensible defaults set for the model specification, but if you choose you can set them yourself if you have a good understanding of what they should be. The mode is set to "regression".
This uses the following engines:
modeltime::nnetar_reg()
nnetar_reg() is a way to generate a specification
of an NNETAR model before fitting and allows the model to be created using
different packages. Currently the only package is forecast.
"nnetar"
Returns a workflowsets object.
Steven P. Sanderson II, MPH
https://workflowsets.tidymodels.org/
https://business-science.github.io/modeltime/reference/nnetar_reg.html
Other Auto Workflowsets:
ts_wfs_arima_boost()
,
ts_wfs_auto_arima()
,
ts_wfs_ets_reg()
,
ts_wfs_lin_reg()
,
ts_wfs_mars()
,
ts_wfs_prophet_reg()
,
ts_wfs_svm_poly()
,
ts_wfs_svm_rbf()
,
ts_wfs_xgboost()
suppressPackageStartupMessages(library(modeltime))
suppressPackageStartupMessages(library(timetk))
suppressPackageStartupMessages(library(dplyr))
suppressPackageStartupMessages(library(rsample))
data <- AirPassengers %>%
ts_to_tbl() %>%
select(-index)
splits <- time_series_split(
data
, date_col
, assess = 12
, skip = 3
, cumulative = TRUE
)
rec_objs <- ts_auto_recipe(
.data = training(splits)
, .date_col = date_col
, .pred_col = value
)
wf_sets <- ts_wfs_nnetar_reg("nnetar", rec_objs)
wf_sets
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