tidiers_ets: Tidying methods for ETS (Error, Trend, Seasonal) exponential...

In sweep: Tidy Tools for Forecasting

Tidying methods for ETS (Error, Trend, Seasonal) exponential smoothing modeling of time series

Description

Tidying methods for ETS (Error, Trend, Seasonal) exponential smoothing modeling of time series

Usage

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

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

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

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

Arguments

x	An object of class "ets"
...	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.
timetk_idx	Used with sw_augment and sw_tidy_decomp. When TRUE, uses a timetk index (irregular, typically date or datetime) if present.
rename_index	Used with sw_augment and sw_tidy_decomp. A string representing the name of the index generated.

Value

sw_tidy() returns one row for each model parameter, with two columns:

• term: The smoothing parameters (alpha, gamma) and the initial states (l, s0 through s10)

• estimate: The estimated parameter value

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_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

• level: The level component

• slope: The slope component (Not always present)

• season: The seasonal component (Not always present)

See Also

ets()

Examples

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

fit_ets <- WWWusage %>%
    ets()

sw_tidy(fit_ets)
sw_glance(fit_ets)
sw_augment(fit_ets)
sw_tidy_decomp(fit_ets)

sweep documentation built on July 9, 2023, 7:10 p.m.