tsensembler: Dynamic Ensembles for Time Series Forecasting

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>.

Version: 0.1.0
Imports: xts, zoo, RcppRoll, methods, ranger, glmnet, earth, kernlab, Cubist, gbm, pls, monmlp, doParallel, foreach, xgboost, softImpute
Suggests: testthat
Published: 2020-10-27
Author: Vitor Cerqueira [aut, cre], Luis Torgo [ctb], Carlos Soares [ctb]
Maintainer: Vitor Cerqueira <cerqueira.vitormanuel at gmail.com>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
URL: https://github.com/vcerqueira/tsensembler
NeedsCompilation: no
Citation: tsensembler citation info
Materials: README
CRAN checks: tsensembler results

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Reference manual: tsensembler.pdf
Package source: tsensembler_0.1.0.tar.gz
Windows binaries: r-devel: tsensembler_0.1.0.zip, r-release: tsensembler_0.1.0.zip, r-oldrel: tsensembler_0.1.0.zip
macOS binaries: r-release: tsensembler_0.1.0.tgz, r-oldrel: tsensembler_0.1.0.tgz
Old sources: tsensembler archive

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