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
nnetar_reg( mode = "regression", seasonal_period = NULL, non_seasonal_ar = NULL, seasonal_ar = NULL, hidden_units = NULL, num_networks = NULL, penalty = NULL, epochs = NULL )
A single character string for the type of model. The only possible value for this model is "regression".
A seasonal frequency. Uses "auto" by default. A character phrase of "auto" or time-based phrase of "2 weeks" can be used if a date or date-time variable is provided. See Fit Details below.
The order of the non-seasonal auto-regressive (AR) terms. Often denoted "p" in pdq-notation.
The order of the seasonal auto-regressive (SAR) terms. Often denoted "P" in PDQ-notation.
An integer for the number of units in the hidden model.
Number of networks to fit with different random starting weights. These are then averaged when producing forecasts.
A non-negative numeric value for the amount of weight decay.
An integer for the number of training iterations.
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
"nnetar" (default) - Connects to
The main arguments (tuning parameters) for the model are the parameters in
nnetar_reg() function. 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 (y, p, P = 1, size, repeats = 20, xreg = NULL, lambda = NULL, ## model = NULL, subset = NULL, scale.inputs = TRUE, x = y, ...)
xreg - This is supplied via the parsnip / modeltime
(so don't provide this manually). See Fit Details (below).
size - Is set to 10 by default. This differs from the
P - Are set to 1 by default.
nnet::nnet parameters that are exposed in the
These are key tuning parameters.
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.
Seasonal Period Specification
The period can be non-seasonal (
seasonal_period = 1 or "none") or
yearly seasonal (e.g. For monthly time stamps,
seasonal_period = 12,
seasonal_period = "12 months", or
seasonal_period = "yearly").
There are 3 ways to specify:
seasonal_period = "auto": A seasonal period is selected based on the periodicity of the data (e.g. 12 if monthly)
seasonal_period = 12: A numeric frequency. For example, 12 is common for monthly data
seasonal_period = "1 year": A time-based phrase. For example, "1 year" would convert to 12 for monthly data.
Univariate (No xregs, Exogenous 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$y) will ignore xreg's.
Multivariate (xregs, Exogenous Regressors)
ordered factor, and
numeric data will be used as xregs.
Date and Date-time variables are not used as xregs
character data should be converted to factor.
Xreg Example: Suppose you have 3 features:
date (time stamp),
month.lbl (labeled month as a ordered factor).
month.lbl is an exogenous regressor that can be passed to the
fit(y ~ date + month.lbl) will pass
month.lbl on as an exogenous regressor.
fit_xy(data[,c("date", "month.lbl")], y = data$y) will pass x, where x is a data frame containing
date feature. Only
month.lbl will 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) # ---- NNETAR ---- # Model Spec model_spec <- nnetar_reg() %>% set_engine("nnetar") # Fit Spec set.seed(123) model_fit <- model_spec %>% fit(log(value) ~ date, data = training(splits))#>model_fit#> parsnip model object #> #> Fit time: 251ms #> Series: outcome #> Model: NNAR(1,1,10) #> Call: forecast::nnetar(y = outcome, p = p, P = P, size = size, repeats = repeats, #> xreg = xreg_matrix, decay = decay, maxit = maxit) #> #> Average of 20 networks, each of which is #> a 2-10-1 network with 41 weights #> options were - linear output units #> #> sigma^2 estimated as 0.0005868