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# Sampled Seatbelts data from the R package datasets.
data("data_seatbelts", package = "forecastML")
# Example - Training data for 2 horizon-specific 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)
# User-define model - LASSO
# A user-defined wrapper function for model training that takes the following
# arguments: (1) a horizon-specific 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)
# User-defined prediction function - LASSO
# The predict() wrapper takes two positional arguments. First,
# the returned model from the user-defined modeling function (model_function() above).
# Second, a data.frame of predictors--identical to the datasets returned from
# create_lagged_df(..., type = "train"). The function can return a 1- or 3-column data.frame
# with either (a) point forecasts or (b) point forecasts plus lower and upper forecast
# bounds (column order and column names do not matter).
prediction_function <- function(model, data_features) {
x <- as.matrix(data_features, ncol = ncol(data_features))
data_pred <- data.frame("y_pred" = predict(model, x, s = "lambda.min"))
return(data_pred)
}
# Predict on the validation datasets.
data_valid <- predict(model_results, prediction_function = list(prediction_function),
data = data_train)
# Forecast error metrics for validation datasets.
data_error <- return_error(data_valid)
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