\dontrun{
data("data_seatbelts", package = "forecastML")
data_train <- create_lagged_df(data_seatbelts, type = "train", method = "direct",
outcome_col = 1, lookback = 1:15,
horizons = c(1, 6, 12))
windows <- create_windows(data_train, window_length = 0)
model_fn <- function(data) {
model <- lm(DriversKilled ~ ., data)
}
model_results <- train_model(data_train, windows, model_name = "OLS",
model_function = model_fn)
predict_fn <- function(model, data) {
data_pred <- as.data.frame(predict(model, data))
}
data_fit <- predict(model_results, prediction_function = list(predict_fn), data = data_train)
residuals <- residuals(data_fit)
data_forecast <- create_lagged_df(data_seatbelts, type = "forecast",
method = "direct", outcome_col = 1,
lookback = 1:15, horizons = c(1, 6, 12))
data_forecasts <- predict(model_results, prediction_function = list(predict_fn),
data = data_forecast)
data_forecasts <- combine_forecasts(data_forecasts)
data_forecasts <- calculate_intervals(data_forecasts, residuals, times = 30)
plot(data_forecasts)
}
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