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Tidying methods for BATS and TBATS modeling of time series

Source: R/tidiers_bats.R
tidiers_bats.Rd

Tidying methods for BATS and TBATS modeling of time series

Usage

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

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

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

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

Arguments

x

An object of class "bats" or "tbats"

...

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.

rename_index

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

timetk_idx

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

Value

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

  • term: The various parameters (lambda, alpha, gamma, etc)

  • 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 (NA for bats / tbats)

  • 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

bats(), tbats()

Examples

library(dplyr)
library(forecast)

fit_bats <- WWWusage %>%
    bats()

sw_tidy(fit_bats)
#> # A tibble: 7 × 2
#>   term              estimate
#>   <chr>                <dbl>
#> 1 lambda               1.00 
#> 2 alpha                1.52 
#> 3 beta                NA    
#> 4 damping.parameter   NA    
#> 5 gamma.values        NA    
#> 6 ar.coefficients     NA    
#> 7 ma.coefficients     -0.666
sw_glance(fit_bats)
#> # 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 BATS(1, {…  3.47   709.  727.  732. 0.217  3.47  2.62 0.261  2.13 0.579 0.0445
sw_augment(fit_bats)
#> # A tibble: 100 × 4
#>    index .actual .fitted  .resid
#>    <int>   <dbl>   <dbl>   <dbl>
#>  1     1      88   101.  -12.7  
#>  2     2      84    75.6   8.38 
#>  3     3      85    85.5  -0.453
#>  4     4      85    84.1   0.867
#>  5     5      84    84.8  -0.818
#>  6     6      85    82.7   2.30 
#>  7     7      83    86.3  -3.29 
#>  8     8      85    80.2   4.78 
#>  9     9      88    88.1  -0.125
#> 10    10      89    89.3  -0.284
#> # ℹ 90 more rows