build_committee_set

form

data

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 parameter setting information for training the
base models;

specs

lambda

alpha

depth size how large is the maximum size
of the subsets. If NULL, defaults to no. of predictors
minus one.

depth

aggregation approach for the
set of subsets.

aggregate_subsets

final aggregation approach. How should the
combined opinions be aggregated.

aggregate_hypos

A function for creating the committee set without
the final training of the 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>.

Vitor Cerqueira

tsensembler

Dynamic Ensembles for Time Series Forecasting

Luis Torgo

Carlos Soares

build_committee_set function

object of class <code><a rd-options='' href='model_specs-class'>model_specs-class</a></code>. Contains
the parameter setting information for training the
base models;

build_committee_set: Build committee set

Description

Usage

Arguments