Tidying methods for STL (Seasonal, Trend, Level) decomposition of time series
# S3 method for stl
sw_tidy(x, ...)# S3 method for stl
sw_tidy_decomp(x, timetk_idx = FALSE, rename_index = "index", ...)
# S3 method for stlm
sw_tidy_decomp(x, timetk_idx = FALSE, rename_index = "index", ...)
# S3 method for stlm
sw_glance(x, ...)
# S3 method for stlm
sw_augment(x, data = NULL, rename_index = "index", timetk_idx = FALSE, ...)
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
An object of class "stl"
Not used.
Used with sw_tidy_decomp.
When TRUE, uses a timetk index (irregular, typically date or datetime) if present.
Used with sw_tidy_decomp.
A string representing the name of the index generated.
Used with sw_augment only.
library(dplyr)
library(forecast)
library(sweep)
fit_stl <- USAccDeaths %>%
stl(s.window = "periodic")
sw_tidy_decomp(fit_stl)
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