Dynamic Ensembles for Time Series Forecasting R package tsensembler version 0.1.0 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>. Software Imports: xts, zoo, RcppRoll, methods, ranger, glmnet, earth, kernlab, Cubist, gbm, pls, monmlp, doParallel, foreach, xgboost, softImpute Suggests: testthat Vitor Cerqueira <cerqueira.vitormanuel@gmail.com> Comprehensive R Archive Network (CRAN) Vitor Cerqueira [aut, cre], Luis Torgo [ctb], Carlos Soares [ctb] GPL (>= 2) 2020-10-27 application/tgz https://CRAN.R-project.org/package=tsensembler doi:10.32614/CRAN.package.tsensembler