Description Usage Arguments Value Methods and related functions Examples
View source: R/combine_forecasts.R
The horizonspecific models can either be combined to (a) produce final forecasts for only those horizons at which they were trained (i.e., shorterhorizon models override longerhorizon models when producing final shorthorizon hstepahead forecasts) or (b) produce final forecasts using any combination of horizonspecific models that minimized error over the validation/training dataset.
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... 
One or more objects of class 'forecast_results' from running 
type 
Default: 'horizon'. A character vector of length 1 that identifies the forecast combination method. 
aggregate 
Default 
data_error 
Optional. A list of objects of class 'validation_error' from running 
metric 
Required if 
An S3 object of class 'forecastML' with final hstepahead forecasts.
Forecast combination type:
type = 'horizon'
: 1 final hstepahead forecast is returned for each model object passed in ...
.
type = 'error'
: 1 final hstepahead forecast is returned by selecting, for each forecast horizon,
the model that minimized the chosen error metric at that horizon on the outerloop validation data sets.
Columns in returned 'forecastML' data.frame:
model
: Usersupplied model name in train_model()
.
model_forecast_horizon
: The directforecasting time horizon that the model was trained on.
horizon
: Forecast horizons, 1:h, measured in dataset rows.
forecast_period
: The forecast period in row indices or dates. The forecast period starts at either attributes(create_lagged_df())$data_stop + 1
for row indices or attributes(create_lagged_df())$data_stop + 1 * frequency
for date indices.
"groups"
: If given, the usersupplied groups in create_lagged_df()
.
"outcome_name"_pred
: The final forecasts.
"outcome_name"_pred_lower
: If given, the lower forecast bounds returned by the usersupplied prediction function.
"outcome_name"_pred_upper
: If given, the upper forecast bounds returned by the usersupplied prediction function.
The output of combine_forecasts()
has the following generic S3 methods
plot
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  # Example with "type = 'horizon'".
data("data_seatbelts", package = "forecastML")
horizons < c(1, 3, 12)
lookback < 1:15
data_train < create_lagged_df(data_seatbelts, type = "train", outcome_col = 1,
lookback = lookback, horizon = horizons)
windows < create_windows(data_train, window_length = 0)
model_function < function(data, my_outcome_col) {
model < lm(DriversKilled ~ ., data = data)
return(model)
}
model_results < train_model(data_train, windows, model_name = "LM", model_function)
data_forecast < create_lagged_df(data_seatbelts, type = "forecast", outcome_col = 1,
lookback = lookback, horizon = horizons)
prediction_function < function(model, data_features) {
x < data_features
data_pred < data.frame("y_pred" = predict(model, newdata = x))
return(data_pred)
}
data_forecasts < predict(model_results, prediction_function = list(prediction_function),
data = data_forecast)
data_combined < combine_forecasts(data_forecasts)
plot(data_combined)

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