tidiers_HoltWinters {sweep}

Tidying methods for HoltWinters modeling of time series

Description

These methods tidy HoltWinters models of univariate time series.

Usage

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

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

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

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

Arguments

x

An object of class "HoltWinters"

...

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 (alpha, beta, gamma, and coefficients)

• estimate: The estimated parameter value

sw_glance() returns one row with the following columns:

• model.desc: A description of the model

• 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

• season: The seasonal component

• trend: The trend component

• remainder: observed - (season + trend)

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

See Also

HoltWinters()

Examples

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

fit_hw <- USAccDeaths %>%
    stats::HoltWinters()

sw_tidy(fit_hw)
sw_glance(fit_hw)
sw_augment(fit_hw)
sw_tidy_decomp(fit_hw)