CRAN
tsensembler 0.0.4
Dynamic Ensembles for Time Series Forecasting
Released Apr 13, 2018 by Vitor Cerqueira
Dependencies
forecast 8.3 gbm 2.1.3 RcppRoll 0.2.2 xts 0.10-2 pls 2.6-0 nnet 7.3-12 opera 1.0 earth 4.6.3 softImpute 1.4 glmnet 2.0-16 ranger 0.10.0 Cubist 0.2.2 kernlab 0.9-26 zoo 1.8-1
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