CRAN/E | tsensembler

tsensembler

Dynamic Ensembles for Time Series Forecasting

Installation

About

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.

Citation tsensembler citation info
github.com/vcerqueira/tsensembler

Key Metrics

Version 0.1.0
Published 2020-10-27 1248 days ago
Needs compilation? no
License GPL-2
License GPL-3
CRAN checks tsensembler results

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Maintainer

Maintainer

Vitor Cerqueira

cerqueira.vitormanuel@gmail.com

Authors

Vitor Cerqueira

aut / cre

Luis Torgo

ctb

Carlos Soares

ctb

Material

README
Reference manual
Package source

macOS

r-release

arm64

r-oldrel

arm64

r-release

x86_64

r-oldrel

x86_64

Windows

r-devel

x86_64

r-release

x86_64

r-oldrel

x86_64

Old Sources

tsensembler archive

Imports

xts
zoo
RcppRoll
methods
ranger
glmnet
earth
kernlab
Cubist
gbm
pls
monmlp
doParallel
foreach
xgboost
softImpute

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