train_ade

form

train

a <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> object class. It contains
the parameter setting specifications for training the ensemble;

specs

window size. Number of observations to compute
the recent performance of the base models, according to the
committee ratio omega. Essentially, the top omega
models are selected and weighted at each prediction instance, according
to their performance in the last lambda observations.
Defaults to 50 according to empirical experiments;

lambda

meta loss function - defaults to ae (absolute error)

lfun

algorithm used to train meta models. Defaults to a
random forest (using ranger package)

meta_model_type

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

Base level models are trained according to specs,
and meta level models are trained using a blocked prequential
procedure in out-of-bag samples from the training 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

train_ade function

a <code><a rd-options='' href='model_specs-class'>model_specs-class</a></code> object class. It contains
the parameter setting specifications for training the ensemble;

train_ade: Training procedure of for ADE

Description

Usage

Arguments