This is a boilerplate function to create automatically the following:
recipe
model specification
workflow
calibration tibble and plot
Usage
ts_auto_lm(
.data,
.date_col,
.value_col,
.formula,
.rsamp_obj,
.prefix = "ts_lm",
.bootstrap_final = FALSE
)Arguments
- .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
- .formula
The formula that is passed to the recipe like
value ~ .- .rsamp_obj
The rsample splits object
- .prefix
Default is
ts_lm- .bootstrap_final
Not yet implemented.
Details
This uses parsnip::linear_reg() and sets the engine to lm
See also
https://parsnip.tidymodels.org/reference/linear_reg.html
Other Boiler_Plate:
ts_auto_arima_xgboost(),
ts_auto_arima(),
ts_auto_croston(),
ts_auto_exp_smoothing(),
ts_auto_glmnet(),
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_theta(),
ts_auto_xgboost()
Examples
if (FALSE) {
library(dplyr)
data <- AirPassengers %>%
ts_to_tbl() %>%
select(-index)
splits <- time_series_split(
data
, date_col
, assess = 12
, skip = 3
, cumulative = TRUE
)
ts_lm <- ts_auto_lm(
.data = data,
.date_col = date_col,
.value_col = value,
.rsamp_obj = splits,
.formula = value ~ .,
)
ts_lm$recipe_info
}