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Tidying methods for STL (Seasonal, Trend, Level) decomposition of time series

Source: R/tidiers_stl.R
tidiers_stl.Rd

Tidying methods for STL (Seasonal, Trend, Level) decomposition of time series

Usage

# S3 method for class 'stl'
sw_tidy(x, ...)

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

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

# S3 method for class 'stlm'
sw_glance(x, ...)

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

Arguments

x

An object of class "stl"

...

Not used.

timetk_idx

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

rename_index

Used with sw_tidy_decomp. A string representing the name of the index generated.

data

Used with sw_augment only.

Value

sw_tidy() wraps sw_tidy_decomp()

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

  • season: The seasonal component

  • trend: The trend component

  • remainder: observed - (season + trend)

  • seasadj: observed - season (or trend + remainder)

sw_glance() returns the underlying ETS or ARIMA model's sw_glance() results 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

stats::stl()

Examples

library(dplyr)
library(forecast)
library(sweep)

fit_stl <- USAccDeaths %>%
    stl(s.window = "periodic")

sw_tidy_decomp(fit_stl)
#> # A tibble: 72 × 6
#>    index     observed  season trend remainder seasadj
#>    <yearmon>    <dbl>   <dbl> <dbl>     <dbl>   <dbl>
#>  1 Jan 1973      9007  -820.  9935.    -108.    9827.
#>  2 Feb 1973      8106 -1559.  9881.    -216.    9665.
#>  3 Mar 1973      8928  -760.  9827.    -139.    9688.
#>  4 Apr 1973      9137  -530.  9766.     -98.2   9667.
#>  5 May 1973     10017   335.  9704.     -22.0   9682.
#>  6 Jun 1973     10826   815.  9637.     374.   10011.
#>  7 Jul 1973     11317  1682.  9569.      65.9   9635.
#>  8 Aug 1973     10744   982.  9500.     262.    9762.
#>  9 Sep 1973      9713   -62.8 9431.     345.    9776.
#> 10 Oct 1973      9938   232.  9343.     363.    9706.
#> # ℹ 62 more rows