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 |
Documentation:
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