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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: 1.0.787
Depends: R (≥ 3.5.0)
Imports: dplyr, stats, forecast, mFilter, DescTools, hht, wavelets, KFAS, daltoolbox
Published: 2024-12-04
DOI: 10.32614/CRAN.package.tspredit
Author: Eduardo Ogasawara ORCID iD [aut, ths, cre], Carla Pacheco [aut], Cristiane Gea [aut], Diogo Santos [aut], Rebecca Salles [aut], Vitoria Birindiba [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_1.0.787.tar.gz
Windows binaries: r-devel: tspredit_1.0.787.zip, r-release: tspredit_1.0.787.zip, r-oldrel: tspredit_1.0.787.zip
macOS binaries: r-release (arm64): tspredit_1.0.787.tgz, r-oldrel (arm64): tspredit_1.0.787.tgz, r-release (x86_64): tspredit_1.0.787.tgz, r-oldrel (x86_64): tspredit_1.0.787.tgz
Old sources: tspredit archive

Linking:

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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.