base_ensemble-class

base_ensemble is a S4 class that contains the base models
comprising the ensemble. Besides the base learning algorithms --
<code>base_models</code> -- base_ensemble class contains information
about other meta-data used to compute predictions for new upcoming data.

internal

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>.

Vitor Cerqueira

tsensembler

Dynamic Ensembles for Time Series Forecasting

Luis Torgo

Carlos Soares

base_ensemble-class function

<dl class="dl-horizontal">
<dt><code>base_models</code></dt><dd>a list comprising the base models;</dd></dl><dt><code>pre_weights</code></dt><dd>Normalized relative weights of the base learners according to
their performance on the available data;</dd><dt><code>form</code></dt><dd>formula;</dd><dt><code>colnames</code></dt><dd>names of the columns of the data used to train the base_models;</dd><dt><code>N</code></dt><dd>number of base models;</dd><dt><code>model_distribution</code></dt><dd>base learner distribution with respect to the type of learner.
That is, the number of Decision Trees, SVMs, etc.</dd>

base_ensemble-class: base_ensemble-class

Description

Arguments

Slots