These methods tidy the coefficients of ARIMA models of univariate time series.
# S3 method for Arima
sw_tidy(x, ...)# S3 method for Arima
sw_glance(x, ...)
# S3 method for Arima
sw_augment(x, data = NULL, rename_index = "index", timetk_idx = FALSE, ...)
# S3 method for stlm
sw_tidy(x, ...)
sw_tidy() returns one row for each coefficient in the model,
with five columns:
term: The term in the nonlinear model being estimated and tested
estimate: The estimated coefficient
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() returns the underlying ETS or ARIMA model's sw_tidy()
one row for each coefficient in the model, with five columns:
term: The term in the nonlinear model being estimated and tested
estimate: The estimated coefficient
An object of class "Arima"
Additional parameters (not used)
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.
Used with sw_augment only.
A string representing the name of the index generated.
Used with sw_augment only.
Uses a irregular timetk index if present.
library(dplyr)
library(forecast)
library(sweep)
fit_arima <- WWWusage %>%
auto.arima()
sw_tidy(fit_arima)
sw_glance(fit_arima)
sw_augment(fit_arima)
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