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]