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Tidying methods for Nural Network Time Series models

Source: R/tidiers_nnetar.R
tidiers_nnetar.Rd

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

Usage

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

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

# S3 method for 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)

fit_nnetar <- lynx %>%
    nnetar()

sw_tidy(fit_nnetar)
#> # A tibble: 4 × 2
#>   term  estimate
#>   <chr>    <dbl>
#> 1 m            1
#> 2 p            8
#> 3 P            0
#> 4 size         4
sw_glance(fit_nnetar)
#> # A tibble: 1 × 12
#>   model.desc sigma logLik AIC   BIC      ME  RMSE   MAE   MPE  MAPE  MASE   ACF1
#>   <chr>      <dbl> <lgl>  <lgl> <lgl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>  <dbl>
#> 1 NNAR(8,4)   291. NA     NA    NA    0.482  291.  216. -41.2  55.6 0.260 0.0535
sw_augment(fit_nnetar)
#> # A tibble: 114 × 4
#>    index .actual .fitted  .resid
#>    <dbl>   <dbl>   <dbl>   <dbl>
#>  1  1821     269     NA   NA    
#>  2  1822     321     NA   NA    
#>  3  1823     585     NA   NA    
#>  4  1824     871     NA   NA    
#>  5  1825    1475     NA   NA    
#>  6  1826    2821     NA   NA    
#>  7  1827    3928     NA   NA    
#>  8  1828    5943     NA   NA    
#>  9  1829    4950   4525. 425.   
#> 10  1830    2577   2576.   0.913
#> # ℹ 104 more rows