The hardware and bandwidth for this mirror is donated by METANET, the Webhosting and Full Service-Cloud Provider.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]metanet.ch.

tspredit: Time Series Prediction Integrated Tuning

Prediction is one of the most important activities while working with time series. There are many alternative ways to model the time series. Finding the right one is challenging to model them. Most data-driven models (either statistical or machine learning) demand tuning. Setting them right is mandatory for good predictions. It is even more complex since time series prediction also demands choosing a data pre-processing that complies with the chosen model. Many time series frameworks have features to build and tune models. The package differs as it provides a framework that seamlessly integrates tuning data pre-processing activities with the building of models. The package provides functions for defining and conducting time series prediction, including data pre(post)processing, decomposition, tuning, modeling, prediction, and accuracy assessment. More information is available at Izau et al. <doi:10.5753/sbbd.2022.224330>.

Version: 1.0.777
Depends: R (≥ 3.5.0)
Imports: dplyr, stats, forecast, mFilter, DescTools, hht, wavelets, KFAS, daltoolbox
Published: 2024-07-29
DOI: 10.32614/CRAN.package.tspredit
Author: Eduardo Ogasawara ORCID iD [aut, ths, cre], Cristiane Gea [aut], Diogo Santos [aut], Rebecca Salles [aut], Vitoria Birindiba [aut], Carla Pacheco [aut], Eduardo Bezerra [aut], Esther Pacitti [aut], Fabio Porto [aut], Federal Center for Technological Education of Rio de Janeiro (CEFET/RJ) [cph]
Maintainer: Eduardo Ogasawara <eogasawara at ieee.org>
License: MIT + file LICENSE
URL: https://github.com/cefet-rj-dal/daltoolbox, https://cefet-rj-dal.github.io/daltoolbox/
NeedsCompilation: no
Materials: README
CRAN checks: tspredit results

Documentation:

Reference manual: tspredit.pdf

Downloads:

Package source: tspredit_1.0.777.tar.gz
Windows binaries: r-devel: tspredit_1.0.777.zip, r-release: tspredit_1.0.777.zip, r-oldrel: tspredit_1.0.777.zip
macOS binaries: r-release (arm64): tspredit_1.0.777.tgz, r-oldrel (arm64): tspredit_1.0.777.tgz, r-release (x86_64): tspredit_1.0.777.tgz, r-oldrel (x86_64): tspredit_1.0.777.tgz
Old sources: tspredit archive

Linking:

Please use the canonical form https://CRAN.R-project.org/package=tspredit to link to this page.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.