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>.

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Description file content

Package
tsensembler
Title
Dynamic Ensembles for Time Series Forecasting
Version
0.0.4
Author
Vitor Cerqueira [aut, cre], Luis Torgo [ctb], Carlos Soares [ctb]
Maintainer
Vitor Cerqueira
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 .
Imports
xts, RcppRoll, methods, ranger, glmnet, earth, kernlab, Cubist, nnet, gbm, zoo, pls, forecast, opera, softImpute
Suggests
testthat
License
GPL (>= 2)
Encoding
UTF-8
LazyData
true
RoxygenNote
6.0.1
URL
NeedsCompilation
no
Packaged
2018-04-13 19:00:14 UTC; root
Repository
CRAN
Date/Publication
2018-04-13 20:11:02 UTC

install.packages('tsensembler')

0.0.4

5 months ago

http://github.com/vcerqueira/tsensembler

Vitor Cerqueira

GPL (>= 2)

Imports

xts, RcppRoll, methods, ranger, glmnet, earth, kernlab, Cubist, nnet, gbm, zoo, pls, forecast, opera, softImpute

Suggests

testthat

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