vcerqueira/tsensembler: Dynamic Ensembles for Time Series Forecasting
Version 0.0.2

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]
MaintainerVitor Cerqueira <[email protected]>
LicenseGPL (>=2)
Version0.0.2
URL http://github.com/vcerqueira/tsensembler
Package repositoryView on GitHub
Installation Install the latest version of this package by entering the following in R:
install.packages("devtools")
library(devtools)
install_github("vcerqueira/tsensembler")
vcerqueira/tsensembler documentation built on Dec. 21, 2017, 5:35 p.m.