loss {opera}

Errors suffered by a sequence of predictions

Description

The function loss computes the sequence of instantaneous losses suffered by the predictions in x to predict the observation in y.

Usage

loss(
  x,
  y,
  pred = NULL,
  loss.type = list(name = "square"),
  loss.gradient = FALSE
)

Arguments

x

numeric. A vector of length T containing the sequence of prediction to be evaluated.

y

numeric. A vector of length T that contains the observations to be predicted.

pred

numeric. A vector of length T containing the sequence of real values.

loss.type

character, list or function ("square").

• character Name of the loss to be applied ('square', 'absolute', 'percentage', or 'pinball');

• list List with field name equal to the loss name. If using pinball loss, field tau equal to the required quantile in [0,1];

• function A custom loss as a function of two parameters.

loss.gradient

boolean, function (TRUE).

• boolean If TRUE, the aggregation rule will not be directly applied to the loss function at hand, but to a gradient version of it. The aggregation rule is then similar to gradient descent aggregation rule.

• function If loss.type is a function, the derivative should be provided to be used (it is not automatically computed).

Value

A vector of length T containing the sequence of instantaneous losses suffered by the expert previsions (x) or the gradient computed on the aggregated previsions (pred).

Author(s)

Pierre Gaillard <[e-mail]>