tidiers_nnetar {sweep}

Tidying methods for Nural Network Time Series models

Description

These methods tidy the coefficients of NNETAR models of univariate time series.

Usage

## S3 method for class 'nnetar'
sw_tidy(x, ...)

## S3 method for class 'nnetar'
sw_glance(x, ...)

## S3 method for class 'nnetar'
sw_augment(x, data = NULL, timetk_idx = FALSE, rename_index = "index", ...)

Arguments

x

An object of class "nnetar"

...

Additional parameters (not used)

data

Used with sw_augment only. NULL by default which simply returns augmented columns only. User can supply the original data, which returns the data + augmented columns.

timetk_idx

Used with sw_augment only. Uses a irregular timetk index if present.

rename_index

Used with sw_augment only. A string representing the name of the index generated.

Value

sw_tidy() returns one row for each model parameter, with two columns:

• term: The smoothing parameters (alpha, gamma) and the initial states (l, s0 through s10)

• estimate: The estimated parameter value

sw_glance() returns one row with the columns

• model.desc: A description of the model including the three integer components (p, d, q) are the AR order, the degree of differencing, and the MA order.

• sigma: The square root of the estimated residual variance

• logLik: The data's log-likelihood under the model (NA)

• AIC: The Akaike Information Criterion (NA)

• BIC: The Bayesian Information Criterion (NA)

• ME: Mean error

• RMSE: Root mean squared error

• MAE: Mean absolute error

• MPE: Mean percentage error

• MAPE: Mean absolute percentage error

• MASE: Mean absolute scaled error

• ACF1: Autocorrelation of errors at lag 1

sw_augment() returns a tibble with the following time series attributes:

• index: An index is either attempted to be extracted from the model or a sequential index is created for plotting purposes

• .actual: The original time series

• .fitted: The fitted values from the model

• .resid: The residual values from the model

See Also

nnetar()

Examples

library(dplyr)
library(forecast)
library(sweep)

fit_nnetar <- lynx %>%
    nnetar()

sw_tidy(fit_nnetar)
sw_glance(fit_nnetar)
sw_augment(fit_nnetar)