intraining_estimations

train

test

form

object of class <code><a rd-options="" href="/link/model_specs-class?package=tsensembler&version=0.0.5" data-mini-rdoc="tsensembler::model_specs-class">model_specs-class</a></code>. Contains
the specifications of the base models.

specs

loss function for metalearning. Defaults to ae -- absolute error.

lfun

A numeric value to specify the number of cores used to
train base and meta models. num_cores = 1
leads to sequential training of models. num_cores &gt; 1
splits the training of the base models across num_cores cores.

num_cores

A pipeline for retrieving out-of-bag loss estimations

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

intraining_estimations function

object of class <code><a rd-options='' href='model_specs-class'>model_specs-class</a></code>. Contains
the specifications of the base models.

intraining_estimations: Out-of-bag loss estimations

Description

Usage

Arguments

Value

See Also