| tidiers_robets {sweep} | R Documentation |
Tidying methods for robets (Robust Error, Trend, Seasonal) exponential smoothing modeling of time series
## S3 method for class 'robets' sw_tidy(x, ...) ## S3 method for class 'robets' sw_glance(x, ...) ## S3 method for class 'robets' sw_augment(x, data = NULL, timetk_idx = FALSE, rename_index = "index", ...) ## S3 method for class 'robets' sw_tidy_decomp(x, timetk_idx = FALSE, rename_index = "index", ...)
x |
An object of class "robets" |
... |
Not used. |
data |
Used with |
timetk_idx |
Used with |
rename_index |
Used with |
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)
library(dplyr)
library(robets)
library(sweep)
fit_robets <- WWWusage %>%
robets()
sw_tidy(fit_robets)
sw_glance(fit_robets)
sw_augment(fit_robets)
sw_tidy_decomp(fit_robets)