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Tidying methods for StructTS (Error, Trend, Seasonal) / exponential smoothing modeling of time series

Source: R/tidiers_StructTS.R
tidiers_StructTS.Rd

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

Usage

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

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

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

Arguments

x

An object of class "StructTS"

...

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 model parameters

  • 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

  • AIC: The Akaike Information Criterion

  • BIC: The Bayesian Information Criterion

  • 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

StructTS()

Examples

library(dplyr)
library(forecast)

fit_StructTS <- WWWusage %>%
    StructTS()

sw_tidy(fit_StructTS)
#> # A tibble: 3 × 2
#>   term    estimate
#>   <chr>      <dbl>
#> 1 level        0  
#> 2 slope       13.0
#> 3 epsilon      0  
sw_glance(fit_StructTS)
#> # A tibble: 1 × 12
#>   model.desc      sigma logLik   AIC   BIC      ME  RMSE   MAE   MPE  MAPE  MASE
#>   <chr>           <dbl>  <dbl> <dbl> <dbl>   <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 Local linear s… 0.995  -277.  559.  564. -0.0200  3.59  2.96 0.140  2.32 0.654
#> # ℹ 1 more variable: ACF1 <dbl>
sw_augment(fit_StructTS)
#> # A tibble: 100 × 4
#>    index .actual .fitted .resid
#>    <int>   <dbl>   <dbl>  <dbl>
#>  1     1      88    88     0   
#>  2     2      84    88.0  -4.00
#>  3     3      85    80     5   
#>  4     4      85    86    -1   
#>  5     5      84    85    -1   
#>  6     6      85    83     2   
#>  7     7      83    86    -3   
#>  8     8      85    81     4   
#>  9     9      88    87     1   
#> 10    10      89    91    -2   
#> # ℹ 90 more rows