TSPred: Functions for Benchmarking Time Series Prediction

Functions for defining and conducting a time series prediction process including pre(post)processing, decomposition, modelling, prediction and accuracy assessment. The generated models and its yielded prediction errors can be used for benchmarking other time series prediction methods and for creating a demand for the refinement of such methods. For this purpose, benchmark data from prediction competitions may be used.

Version: 5.1
Depends: R (≥ 3.5.0)
Imports: forecast, KFAS, stats, MuMIn, EMD, wavelets, vars, ModelMetrics, RSNNS, Rlibeemd, e1071, elmNNRcpp, nnet, randomForest, magrittr, plyr, methods, dplyr, keras, tfdatasets
Published: 2021-01-21
DOI: 10.32614/CRAN.package.TSPred
Author: Rebecca Pontes Salles [aut, cre, cph] (CEFET/RJ), Eduardo Ogasawara [ths] (CEFET/RJ)
Maintainer: Rebecca Pontes Salles <rebeccapsalles at acm.org>
BugReports: https://github.com/RebeccaSalles/TSPred/issues
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
URL: https://github.com/RebeccaSalles/TSPred/wiki
NeedsCompilation: no
Citation: TSPred citation info
CRAN checks: TSPred results


Reference manual: TSPred.pdf


Package source: TSPred_5.1.tar.gz
Windows binaries: r-devel: TSPred_5.1.zip, r-release: TSPred_5.1.zip, r-oldrel: TSPred_5.1.zip
macOS binaries: r-release (arm64): TSPred_5.1.tgz, r-oldrel (arm64): TSPred_5.1.tgz, r-release (x86_64): TSPred_5.1.tgz, r-oldrel (x86_64): TSPred_5.1.tgz
Old sources: TSPred archive

Reverse dependencies:

Reverse imports: predtoolsTS


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