TSPred: Functions for Benchmarking Time Series Prediction

Functions for time series preprocessing, decomposition, prediction and accuracy assessment using automatic linear modelling. The generated linear 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: 4.0
Depends: R (≥ 2.10)
Imports: forecast, KFAS, stats, MuMIn, EMD, wavelets, vars
Published: 2018-06-21
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_4.0.tar.gz
Windows binaries: r-devel: TSPred_4.0.zip, r-devel-gcc8: TSPred_4.0.zip, r-release: TSPred_4.0.zip, r-oldrel: TSPred_4.0.zip
OS X binaries: r-release: TSPred_4.0.tgz, r-oldrel: TSPred_4.0.tgz
Old sources: TSPred archive

Reverse dependencies:

Reverse imports: predtoolsTS


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