model_recent_performance: Recent performance of models using EMASE

In tsensembler: Dynamic Ensembles for Time Series Forecasting

Description

This function computes EMASE, Erfc Moving Average Squared Error, to quantify the recent performance of the base models.

Usage

model_recent_performance(Y_hat, Y, lambda, omega, pre_weights)

Arguments

Y_hat	A data.frame containing the predictions of each base model;
Y	know true values from past data to compare the predictions to;
lambda	Window size. Number of periods to average over when computing MASE;
omega	Ratio of top models in the committee;
pre_weights	The initial weights of the models, computed in the available data during the learning phase;

Value

A list containing two objects:

model_scores

The weights of the models in each time point

top_models

Models in the committee in each time point

See Also

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

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