return_error: Compute forecast error
In forecastML: Time Series Forecasting with Machine Learning Methods

Description Usage Arguments Value Error Metrics Methods and related functions Examples

Compute forecast error metrics on the validation datasets or a new test dataset.

return_error(
  data_results,
  data_test = NULL,
  test_indices = NULL,
  aggregate = stats::median,
  metrics = c("mae", "mape", "mdape", "smape", "rmse", "rmsse"),
  models = NULL,
  horizons = NULL,
  windows = NULL,
  group_filter = NULL
)

`data_results`	An object of class 'training_results' or 'forecast_results' from running (a) `predict` on a trained model or (b) `combine_forecasts()`.
`data_test`	Required for forecast results only. If `data_results` is an object of class 'forecast_results', a data.frame used to assess the accuracy of a 'forecast_results' object. `data_test` should have the outcome/target columns and any grouping columns.
`test_indices`	Required if `data_test` is given or 'rmsse' row indices or dates (class 'Date' or 'POSIXt') with length `nrow(data_test)`.
`aggregate`	Default `median`. A function–without parentheses–that aggregates historical prediction or forecast error across time series. All error metrics are first calculated at the level of the individual time series. `aggregate` is then used to combine error metrics across validation windows and horizons. Aggregations are returned at the group level if `data_results` contains groups.
`metrics`	A character vector of common forecast error metrics. The default behavior is to return all metrics.
`models`	Optional. A character vector of user-defined model names supplied to `train_model()` to filter results.
`horizons`	Optional. A numeric vector to filter results by horizon.
`windows`	Optional. A numeric vector to filter results by validation window number.
`group_filter`	Optional. A string for filtering plot results for grouped time series (e.g., `"group_col_1 == 'A'"`). `group_filter` is passed to `dplyr::filter()` internally.

An S3 object of class 'validation_error', 'forecast_error', or 'forecastML_error': A list of data.frames of error metrics for the validation or forecast dataset depending on the class of data_results: 'training_results', 'forecast_results', or 'forecastML' from combine_forecasts().

A list containing:

Error metrics by model, horizon, and validation window
Error metrics by model and horizon, collapsed across validation windows
Global error metrics by model collapsed across horizons and validation windows

mae: Mean absolute error (works with factor outcomes)
mape: Mean absolute percentage error
mdape: Median absolute percentage error
smape: Symmetrical mean absolute percentage error
rmse: Root mean squared error
rmsse: Root mean squared scaled error from the M5 competition

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

plot from return_error()
plot from return_error()

# 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)