combine_forecasts: Combine multiple horizon-specific forecast models to produce...
In nredell/forecastML: Time Series Forecasting with Machine Learning Methods

Description Usage Arguments Value Methods and related functions Examples

The horizon-specific models can either be combined to (a) produce final forecasts for only those horizons at which they were trained (i.e., shorter-horizon models override longer-horizon models when producing final short-horizon h-step-ahead forecasts) or (b) produce final forecasts using any combination of horizon-specific models that minimized error over the validation/training dataset.

combine_forecasts(
  ...,
  type = c("horizon", "error"),
  aggregate = stats::median,
  data_error = list(NULL),
  metric = NULL
)

`...`	One or more objects of class 'forecast_results' from running `predict.forecast_model()` on an input forward-looking forecast dataset. These are the forecasts from the horizon-specific direct forecasting models trained over the entire training dataset by setting `create_windows(..., window_length = 0)`. If multiple models are passed in `...` with the same direct forecast horizon, for `type = 'horizon'`, forecasts for the same direct forecast horizon are combined with `aggregate`; for `type = 'error'`, the model that minimizes the error metric at the given direct forecast horizon produces the forecast.
`type`	Default: 'horizon'. A character vector of length 1 that identifies the forecast combination method.
`aggregate`	Default `median` for `type = 'horizon'`. A function–without parentheses–that aggregates forecasts if more than one model passed in `...` has the same direct forecast horizon and `type = 'horizon'].`
`data_error`	Optional. A list of objects of class 'validation_error' from running `return_error()` on a training dataset. The length and order of `data_error` should match the models passed in `...`.
`metric`	Required if `data_error` is given. A length 1 character vector naming the forecast error metric used to select the optimal model at each forecast horizon from the models passed in '...' e.g., 'mae'.

An S3 object of class 'forecastML' with final h-step-ahead forecasts.

Forecast combination type:

type = 'horizon': 1 final h-step-ahead forecast is returned for each model object passed in ....
type = 'error': 1 final h-step-ahead forecast is returned by selecting, for each forecast horizon, the model that minimized the chosen error metric at that horizon on the outer-loop validation data sets.

Columns in returned 'forecastML' data.frame:

model: User-supplied model name in train_model().
model_forecast_horizon: The direct-forecasting 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 user-supplied groups in create_lagged_df().
"outcome_name"_pred: The final forecasts.
"outcome_name"_pred_lower: If given, the lower forecast bounds returned by the user-supplied prediction function.
"outcome_name"_pred_upper: If given, the upper forecast bounds returned by the user-supplied prediction function.

The output of combine_forecasts() has the following generic S3 methods

plot

# 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)