`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`

.

## Usage

```
nnetar_reg(
mode = "regression",
seasonal_period = NULL,
non_seasonal_ar = NULL,
seasonal_ar = NULL,
hidden_units = NULL,
num_networks = NULL,
penalty = NULL,
epochs = NULL
)
```

## Arguments

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

- seasonal_period
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.

- 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.

## Details

The data given to the function are not saved and are only used
to determine the *mode* of the model. For `nnetar_reg()`

, the
mode will always be "regression".

The model can be created using the `fit()`

function using the
following *engines*:

"nnetar" (default) - Connects to

`forecast::nnetar()`

**Main Arguments**

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 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:

modeltime | forecast::nnetar |

seasonal_period | ts(frequency) |

non_seasonal_ar | p (1) |

seasonal_ar | P (1) |

hidden_units | size (10) |

num_networks | repeats (20) |

epochs | maxit (100) |

penalty | decay (0) |

Other options can be set using `set_engine()`

.

**nnetar**

The engine uses `forecast::nnetar()`

.

Function Parameters:

```
#> function (y, p, P = 1, size, repeats = 20, xreg = NULL, lambda = NULL,
#> model = NULL, subset = NULL, scale.inputs = TRUE, x = y, ...)
```

Parameter Notes:

`xreg`

- This is supplied via the parsnip / modeltime`fit()`

interface (so don't provide this manually). See Fit Details (below).`size`

- Is set to 10 by default. This differs from the`forecast`

implementation`p`

and`P`

- Are set to 1 by default.`maxit`

and`decay`

are`nnet::nnet`

parameters that are exposed in the`nnetar_reg()`

interface. These are key tuning parameters.

## 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)`

*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.XY Interface:

`fit_xy(x = data[,"date"], y = data$y)`

will ignore xreg's.

**Multivariate (xregs, Exogenous Regressors)**

The `xreg`

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 `nnetar_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(parsnip)
library(rsample)
library(timetk)
# 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
#> # ℹ 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))
#> frequency = 12 observations per 1 year
model_fit
#> parsnip model object
#>
#> Series: outcome
#> Model: NNAR(1,1,10)[12]
#> 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 0.0005869
```