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

Source: R/tidiers_ets.R
tidiers_ets.Rd

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

Usage

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

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

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

# S3 method for ets
sw_tidy_decomp(x, timetk_idx = FALSE, rename_index = "index", ...)

Arguments

x

An object of class "ets"

...

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 and sw_tidy_decomp. When TRUE, uses a timetk index (irregular, typically date or datetime) if present.

rename_index

Used with sw_augment and sw_tidy_decomp. 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

  • 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

sw_tidy_decomp() 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

  • observed: The original time series

  • level: The level component

  • slope: The slope component (Not always present)

  • season: The seasonal component (Not always present)

See also

ets()

Examples

library(dplyr)
library(forecast)

fit_ets <- WWWusage %>%
    ets()

sw_tidy(fit_ets)
#> # A tibble: 5 × 2
#>   term  estimate
#>   <chr>    <dbl>
#> 1 alpha   1.00  
#> 2 beta    0.997 
#> 3 phi     0.815 
#> 4 l      90.4   
#> 5 b      -0.0173
sw_glance(fit_ets)
#> # A tibble: 1 × 12
#>   model.desc  sigma logLik   AIC   BIC    ME  RMSE   MAE   MPE  MAPE  MASE  ACF1
#>   <chr>       <dbl>  <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 ETS(A,Ad,N)  3.50  -353.  718.  733. 0.224  3.41  2.76 0.263  2.16 0.610 0.231
sw_augment(fit_ets)
#> # A tibble: 100 × 4
#>    index .actual .fitted .resid
#>    <int>   <dbl>   <dbl>  <dbl>
#>  1     1      88    90.3 -2.34 
#>  2     2      84    86.1 -2.09 
#>  3     3      85    80.7  4.25 
#>  4     4      85    85.8 -0.803
#>  5     5      84    85.0 -1.00 
#>  6     6      85    83.2  1.81 
#>  7     7      83    85.8 -2.81 
#>  8     8      85    81.4  3.62 
#>  9     9      88    86.6  1.38 
#> 10    10      89    90.4 -1.44 
#> # ℹ 90 more rows
sw_tidy_decomp(fit_ets)
#> # A tibble: 101 × 4
#>    index observed level    slope
#>    <dbl>    <dbl> <dbl>    <dbl>
#>  1     0       NA  90.4 -0.0173 
#>  2     1       88  88.0 -2.34   
#>  3     2       84  84.0 -3.99   
#>  4     3       85  85.0  0.986  
#>  5     4       85  85.0  0.00312
#>  6     5       84  84.0 -0.997  
#>  7     6       85  85.0  0.994  
#>  8     7       83  83.0 -1.99   
#>  9     8       85  85.0  1.99   
#> 10     9       88  88.0  3.00   
#> # ℹ 91 more rows