EMASE {tsensembler}R Documentation

Weighting Base Models by their Moving Average Squared Error

Description

This function computes the weights of the learning models using the Moving Average Squared Error (MASE) function This method provides a simple way to quantify the recent performance of each base learner and adapt the combined model accordingly.

Usage

EMASE(loss, lambda, pre_weights)

Arguments

loss

Squared error of the models at each test point;

lambda

Number of periods to average over when computing MASE;

pre_weights

pre-weights of the base models computed in the train set.

Value

The weights of the models in test time.

See Also

Other weighting base models: build_committee(), get_top_models(), model_recent_performance(), model_weighting(), select_best()


[Package tsensembler version 0.1.0 Index]