Package: tsensembler
Title: Dynamic Ensembles for Time Series Forecasting
Version: 0.1.0
Authors@R: c(person("Vitor", "Cerqueira", email = "cerqueira.vitormanuel@gmail.com", role = c("aut", "cre")),
			 person("Luis", "Torgo", role = "ctb"),
       person("Carlos", "Soares", role = "ctb"))
Author: Vitor Cerqueira [aut, cre],
  Luis Torgo [ctb],
  Carlos Soares [ctb]
Maintainer: Vitor Cerqueira <cerqueira.vitormanuel@gmail.com>
Description: 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>.
Imports: xts, zoo, RcppRoll, methods, ranger, glmnet, earth, kernlab,
        Cubist, gbm, pls, monmlp, doParallel, foreach, xgboost,
        softImpute
Suggests: testthat
License: GPL (>= 2)
Encoding: UTF-8
LazyData: true
RoxygenNote: 7.1.1
URL: https://github.com/vcerqueira/tsensembler
NeedsCompilation: no
Packaged: 2020-10-26 09:31:22 UTC; root
Repository: CRAN
Date/Publication: 2020-10-27 14:00:02 UTC
Built: R 4.6.0; ; 2026-04-26 10:54:59 UTC; unix
