EMASE: Weighting Base Models by their Moving Average Squared Error

In tsensembler: Dynamic Ensembles for Time Series Forecasting

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()

tsensembler documentation built on Oct. 27, 2020, 5:07 p.m.