tidiers_stl: Tidying methods for STL (Seasonal, Trend, Level)...

In business-science/sweep: Tidy Tools for Forecasting

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

Description

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

stl()

Examples

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

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

sw_tidy_decomp(fit_stl)

business-science/sweep documentation built on Feb. 2, 2024, 2:49 a.m.