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# CITATION file created with {cffr} R package
# See also: https://docs.ropensci.org/cffr/
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cff-version: 1.2.0
message: 'To cite package "tspredit" in publications use:'
type: software
license: MIT
title: 'tspredit: Time Series Prediction with Integrated Tuning'
version: 2.0.707
doi: 10.32614/CRAN.package.tspredit
abstract: Time series prediction is a critical task in data analysis, requiring not
  only the selection of appropriate models, but also suitable data preprocessing and
  tuning strategies. TSPredIT (Time Series Prediction with Integrated Tuning) is a
  framework that provides a seamless integration of data preprocessing, decomposition,
  model training, hyperparameter optimization, and evaluation. Unlike other frameworks,
  TSPredIT emphasizes the co-optimization of both preprocessing and modeling steps,
  improving predictive performance. It supports a variety of statistical and machine
  learning models, filtering techniques, outlier detection, data augmentation, and
  ensemble strategies. More information is available in Salles et al. <https://doi.org/10.1007/978-3-662-68014-8_2>.
authors:
- family-names: Ogasawara
  given-names: Eduardo
  email: eogasawara@ieee.org
  orcid: https://orcid.org/0000-0002-0466-0626
- family-names: Gea
  given-names: Cristiane
  email: cristiane.gea@eic.cefet-rj.br
- family-names: Santos
  given-names: Diogo
  email: diogo.santos@eic.cefet-rj.br
- family-names: Garcia
  given-names: Arthur
  email: arthur.garcia@eic.cefet-rj.br
- family-names: Alexandrino
  given-names: Fernando
  email: fernando.alexandrino@ifsp.edu.br
- family-names: Salles
  given-names: Rebecca
  email: rebecca.salles@eic.cefet-rj.br
- family-names: Birindiba
  given-names: Vitoria
  email: vitoria.birindiba@eic.cefet-rj.br
repository: https://cefet-rj-dal.r-universe.dev
repository-code: https://github.com/cefet-rj-dal/tspredit
commit: 83874f341eb19efc3888d6c740c62a58e09d3e6c
url: https://cefet-rj-dal.github.io/tspredit/
date-released: '2026-05-22'
contact:
- family-names: Ogasawara
  given-names: Eduardo
  email: eogasawara@ieee.org
  orcid: https://orcid.org/0000-0002-0466-0626