View source: R/boilerplate-theta.R
ts_auto_theta | R Documentation |
This is a boilerplate function to create automatically the following:
recipe
model specification
workflow
calibration tibble and plot
ts_auto_theta(
.data,
.date_col,
.value_col,
.rsamp_obj,
.prefix = "ts_theta",
.bootstrap_final = FALSE
)
.data |
The data being passed to the function. The time-series object. |
.date_col |
The column that holds the datetime. |
.value_col |
The column that has the value |
.rsamp_obj |
The splits object |
.prefix |
Default is |
.bootstrap_final |
Not yet implemented. |
This uses the forecast::thetaf()
for the parsnip
engine. This
model does not use exogenous regressors, so only a univariate model of: value ~ date
will be used from the .date_col
and .value_col
that you provide.
A list
Steven P. Sanderson II, MPH
https://business-science.github.io/modeltime/reference/exp_smoothing.html#engine-details
https://pkg.robjhyndman.com/forecast/reference/thetaf.html
Other Boiler_Plate:
ts_auto_arima()
,
ts_auto_arima_xgboost()
,
ts_auto_croston()
,
ts_auto_exp_smoothing()
,
ts_auto_glmnet()
,
ts_auto_lm()
,
ts_auto_mars()
,
ts_auto_nnetar()
,
ts_auto_prophet_boost()
,
ts_auto_prophet_reg()
,
ts_auto_smooth_es()
,
ts_auto_svm_poly()
,
ts_auto_svm_rbf()
,
ts_auto_xgboost()
Other exp_smoothing:
ts_auto_croston()
,
ts_auto_exp_smoothing()
,
ts_auto_smooth_es()
library(dplyr)
library(timetk)
library(modeltime)
data <- AirPassengers %>%
ts_to_tbl() %>%
select(-index)
splits <- time_series_split(
data
, date_col
, assess = 12
, skip = 3
, cumulative = TRUE
)
ts_theta <- ts_auto_theta(
.data = data,
.date_col = date_col,
.value_col = value,
.rsamp_obj = splits
)
ts_theta$recipe_info
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