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Tidying methods for ARIMA modeling of time series

Source: R/tidiers_arima.R, R/tidiers_stl.R
tidiers_arima.Rd

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

Usage

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

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

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

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

Arguments

x

An object of class "Arima"

...

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 only. Uses a irregular timetk index if present.

Value

sw_tidy() returns one row for each coefficient in the model, with five columns:

  • term: The term in the nonlinear model being estimated and tested

  • estimate: The estimated coefficient

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() returns the underlying ETS or ARIMA model's sw_tidy()

one row for each coefficient in the model, with five columns:

  • term: The term in the nonlinear model being estimated and tested

  • estimate: The estimated coefficient

See also

arima(), Arima()

Examples

library(dplyr)
library(forecast)

fit_arima <- WWWusage %>%
    auto.arima()

sw_tidy(fit_arima)
#> # A tibble: 2 × 2
#>   term  estimate
#>   <chr>    <dbl>
#> 1 ar1      0.650
#> 2 ma1      0.526
sw_glance(fit_arima)
#> # 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 ARIMA(1,1,1)  3.16  -254.  514.  522. 0.304  3.11  2.41 0.281  1.92 0.532
#> # ℹ 1 more variable: ACF1 <dbl>
sw_augment(fit_arima)
#> # A tibble: 100 × 4
#>    index .actual .fitted  .resid
#>    <int>   <dbl>   <dbl>   <dbl>
#>  1     1      88    87.9  0.0880
#>  2     2      84    86.2 -2.17  
#>  3     3      85    81.1  3.86  
#>  4     4      85    87.5 -2.45  
#>  5     5      84    83.7  0.259 
#>  6     6      85    83.5  1.51  
#>  7     7      83    86.4 -3.44  
#>  8     8      85    79.9  5.11  
#>  9     9      88    89.0 -0.985 
#> 10    10      89    89.4 -0.433 
#> # ℹ 90 more rows