tsensembler: Dynamic Ensembles for Time Series Forecasting
Version 0.0.4

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 .

Getting started

Package details

AuthorVitor Cerqueira [aut, cre], Luis Torgo [ctb], Carlos Soares [ctb]
Date of publication2018-04-13 20:11:02 UTC
MaintainerVitor Cerqueira <[email protected]>
LicenseGPL (>= 2)
Version0.0.4
URL http://github.com/vcerqueira/tsensembler
Package repositoryView on CRAN
Installation Install the latest version of this package by entering the following in R:
install.packages("tsensembler")

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tsensembler documentation built on April 14, 2018, 1:03 a.m.