Package: tspredit
Title: Time Series Prediction with Integrated Tuning
Version: 1.1.707
Authors@R: 
  c(
    person(
      given = "Eduardo",
      family = "Ogasawara",
      role = c("aut", "ths", "cre"),
      email = "eogasawara@ieee.org",
      comment = c(ORCID = "0000-0002-0466-0626")
    ),
    person(
      given = "Carla",
      family = "Pacheco",
      role = c("aut"),
      email = "carla.pacheco@eic.cefet-rj.br"
    ),
    person(
      given = "Cristiane",
      family = "Gea",
      role = c("aut"),
      email = "cristiane.gea@eic.cefet-rj.br"
    ),
    person(
      given = "Diogo",
      family = "Santos",
      role = c("aut"),
      email = "diogo.santos@eic.cefet-rj.br"
    ),
    person(
      given = "Rebecca",
      family = "Salles",
      role = c("aut"),
      email = "rebecca.salles@eic.cefet-rj.br"
    ),
    person(
      given = "Vitoria",
      family = "Birindiba",
      role = c("aut"),
      email = "vitoria.birindiba@eic.cefet-rj.br"
    ),
    person(
      given = "Eduardo",
      family = "Bezerra",
      role = c("aut"),
      email = "ebezerra@cefet-rj.br"
    ),
    person(
      given = "Esther",
      family = "Pacitti",
      role = c("aut"),
      email = "Esther.Pacitti@lirmm.fr"
    ),
    person(
      given = "Fabio",
      family = "Porto",
      role = c("aut"),
      email = "fporto@lncc.br"
    ),
    person(
      given = "CEFET/RJ",
      role = "cph",
      comment = c(organization = "Federal Center for Technological Education of Rio de Janeiro")
    )
  )
Description: 
  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. <doi:10.1007/978-3-662-68014-8_2>.
License: MIT + file LICENSE
URL: https://cefet-rj-dal.github.io/tspredit/,
        https://github.com/cefet-rj-dal/tspredit
BugReports: https://github.com/cefet-rj-dal/tspredit/issues
Encoding: UTF-8
RoxygenNote: 7.3.2
Depends: R (>= 4.1.0)
Imports: dplyr, stats, forecast, mFilter, DescTools, hht, wavelets,
        KFAS, daltoolbox
NeedsCompilation: no
Packaged: 2025-04-24 03:52:39 UTC; gpca
Author: Eduardo Ogasawara [aut, ths, cre]
    (<https://orcid.org/0000-0002-0466-0626>),
  Carla Pacheco [aut],
  Cristiane Gea [aut],
  Diogo Santos [aut],
  Rebecca Salles [aut],
  Vitoria Birindiba [aut],
  Eduardo Bezerra [aut],
  Esther Pacitti [aut],
  Fabio Porto [aut],
  CEFET/RJ [cph] (Federal Center for Technological Education of Rio de
    Janeiro)
Maintainer: Eduardo Ogasawara <eogasawara@ieee.org>
Repository: CRAN
Date/Publication: 2025-04-24 11:40:02 UTC
