tsensembler (0.0.4)

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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 .

Maintainer: Vitor Cerqueira
Author(s): Vitor Cerqueira [aut, cre], Luis Torgo [ctb], Carlos Soares [ctb]

License: GPL (>= 2)

Uses: Cubist, earth, forecast, gbm, glmnet, kernlab, nnet, opera, pls, ranger, RcppRoll, softImpute, xts, zoo, testthat

Released 2 months ago.

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