model_recent_performance

A <code>data.frame</code> containing the predictions of
each base model;

Y_hat

know true values from past data to compare the predictions to;

Window size. Number of periods to average
over when computing MASE;

lambda

Ratio of top models in the committee;

omega

The initial weights of the models, computed in
the available data during the learning phase;

pre_weights

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

A framework for dynamically combining forecasting models for time series forecasting predictive tasks. It leverages machine learning models from other packages to automatically combine expert advice using metalearning and other state-of-the-art forecasting combination approaches. The predictive methods receive a data matrix as input, representing an embedded time series, and return a predictive ensemble model. The ensemble use generic functions 'predict()' and 'forecast()' to forecast future values of the time series. Moreover, an ensemble can be updated using methods, such as 'update_weights()' or 'update_base_models()'. A complete description of the methods can be found in: Cerqueira, V., Torgo, L., Pinto, F., and Soares, C. "Arbitrated Ensemble for Time Series Forecasting." to appear at: Joint European Conference on Machine Learning and Knowledge Discovery in Databases. Springer International Publishing, 2017; and Cerqueira, V., Torgo, L., and Soares, C.: "Arbitrated Ensemble for Solar Radiation Forecasting." International Work-Conference on Artificial Neural Networks. Springer, 2017 <doi:10.1007/978-3-319-59153-7_62>.

model_recent_performance: Recent performance of models using EMASE

Description

Usage

Arguments

Value

See Also