These functions are used to construct new modeltime bridge functions that
connect the tidymodels infrastructure to time-series models containing date or date-time features.
Arguments
- class
A class name that is used for creating custom printing messages
- models
A list containing one or more models
- data
A data frame (or tibble) containing 4 columns: (date column with name that matches input data), .actual, .fitted, and .residuals.
- extras
An optional list that is typically used for transferring preprocessing recipes to the predict method.
- desc
An optional model description to appear when printing your modeltime objects
Examples
library(dplyr)
library(lubridate)
library(timetk)
lm_model <- lm(value ~ as.numeric(date) + hour(date) + wday(date, label = TRUE),
data = taylor_30_min)
data = tibble(
date = taylor_30_min$date, # Important - The column name must match the modeled data
# These are standardized names: .actual, .fitted, .residuals
.actual = taylor_30_min$value,
.fitted = lm_model$fitted.values %>% as.numeric(),
.residuals = lm_model$residuals %>% as.numeric()
)
new_modeltime_bridge(
class = "lm_time_series_impl",
models = list(model_1 = lm_model),
data = data,
extras = NULL
)
#> $model_1
#>
#> Call:
#> lm(formula = value ~ as.numeric(date) + hour(date) + wday(date,
#> label = TRUE), data = taylor_30_min)
#>
#> Coefficients:
#> (Intercept) as.numeric(date)
#> 1.489e+05 -1.284e-04
#> hour(date) wday(date, label = TRUE).L
#> 3.919e+02 6.665e+02
#> wday(date, label = TRUE).Q wday(date, label = TRUE).C
#> -5.952e+03 5.479e+02
#> wday(date, label = TRUE)^4 wday(date, label = TRUE)^5
#> -1.955e+03 2.482e+02
#> wday(date, label = TRUE)^6
#> -8.755e+01
#>
#>