Description Usage Arguments Value Methods and related functions Examples
Train a userdefined forecast model for each horizon, 'h', and across the validation
datasets, 'd'. If method = "direct"
, a total of 'h' * 'd' models are trained.
If method = "multi_output"
, a total of 1 * 'd' models are trained.
These models can be trained in parallel with the future
package.
1 2 3 4 5 6 7 8  train_model(
lagged_df,
windows,
model_name,
model_function,
...,
use_future = FALSE
)

lagged_df 
An object of class 'lagged_df' from 
windows 
An object of class 'windows' from 
model_name 
A name for the model. 
model_function 
A userdefined wrapper function for model training that takes the following
arguments: (1) a horizonspecific data.frame made with 
... 
Optional. Named arguments passed into the userdefined 
use_future 
Boolean. If 
An S3 object of class 'forecast_model': A nested list of trained models. Models can be accessed with
my_trained_model$horizon_h$window_w$model
where 'h' gives the forecast horizon and 'w' gives
the validation dataset window number from create_windows()
.
The output of train_model
can be passed into
return_error
return_hyper
and has the following generic S3 methods
predict
plot
(from predict.forecast_model(data = create_lagged_df(..., type = "train"))
)
plot
(from predict.forecast_model(data = create_lagged_df(..., type = "forecast"))
)
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37  # Sampled Seatbelts data from the R package datasets.
data("data_seatbelts", package = "forecastML")
# Example  Training data for 2 horizonspecific models w/ common lags per predictor.
horizons < c(1, 12)
lookback < 1:15
data_train < create_lagged_df(data_seatbelts, type = "train", outcome_col = 1,
lookback = lookback, horizon = horizons)
# One custom validation window at the end of the dataset.
windows < create_windows(data_train, window_start = 181, window_stop = 192)
# Userdefine model  LASSO
# A userdefined wrapper function for model training that takes the following
# arguments: (1) a horizonspecific data.frame made with create_lagged_df(..., type = "train")
# (e.g., my_lagged_df$horizon_h) and, optionally, (2) any number of additional named arguments
# which are passed as '...' in train_model().
library(glmnet)
model_function < function(data, my_outcome_col) {
x < data[, (my_outcome_col), drop = FALSE]
y < data[, my_outcome_col, drop = FALSE]
x < as.matrix(x, ncol = ncol(x))
y < as.matrix(y, ncol = ncol(y))
model < glmnet::cv.glmnet(x, y, nfolds = 3)
return(model)
}
# my_outcome_col = 1 is passed in ... but could have been defined in model_function().
model_results < train_model(data_train, windows, model_name = "LASSO", model_function,
my_outcome_col = 1)
# View the results for the model (a) trained on the first horizon
# and (b) to be assessed on the first outerloop validation window.
model_results$horizon_1$window_1$model

Loading required package: dplyr
Attaching package: ‘dplyr’
The following objects are masked from ‘package:stats’:
filter, lag
The following objects are masked from ‘package:base’:
intersect, setdiff, setequal, union
Loading required package: Matrix
Loaded glmnet 4.02
Call: glmnet::cv.glmnet(x = x, y = y, nfolds = 3)
Measure: MeanSquared Error
Lambda Measure SE Nonzero
min 2.454 301.1 15.91 7
1se 4.288 316.0 17.84 5
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