The hardware and bandwidth for this mirror is donated by dogado GmbH, the Webhosting and Full Service-Cloud Provider. Check out our Wordpress Tutorial.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]dogado.de.

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
DOI: 10.32614/CRAN.package.tsensembler
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

Documentation:

Reference manual: tsensembler.pdf

Downloads:

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 (arm64): tsensembler_0.1.0.tgz, r-oldrel (arm64): tsensembler_0.1.0.tgz, r-release (x86_64): tsensembler_0.1.0.tgz, r-oldrel (x86_64): tsensembler_0.1.0.tgz
Old sources: tsensembler archive

Linking:

Please use the canonical form https://CRAN.R-project.org/package=tsensembler to link to this page.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.