Package: tspredit
Title: Time Series Prediction with Integrated Tuning
Version: 2.0.707
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 = "Cristiane", family = "Gea", role = "aut", email = "cristiane.gea@eic.cefet-rj.br"),
        person(given = "Diego", family = "Carvalho", role = "ctb", email = "diego.carvalho@cefet-rj.br"),
        person(given = "Diogo", family = "Santos", role = "aut", email = "diogo.santos@eic.cefet-rj.br"),
        person(given = "Arthur", family = "Garcia", role = "aut", email = "arthur.garcia@eic.cefet-rj.br"),
        person(given = "Eduardo", family = "Bezerra", role = "ctb", email = "ebezerra@cefet-rj.br"),
        person(given = "Esther", family = "Pacitti", role = "ctb", email = "Esther.Pacitti@lirmm.fr"),
        person(given = "Fabio", family = "Porto", role = "ctb", email = "fporto@lncc.br"),
        person(given = "Fernando", family = "Alexandrino", role = "aut", email = "fernando.alexandrino@ifsp.edu.br"),
        person(given = "Rebecca", family = "Salles", role = "aut", email = "rebecca.salles@eic.cefet-rj.br"),
        person(given = "Vitoria", family = "Birindiba", role = "aut", email = "vitoria.birindiba@eic.cefet-rj.br"),
        person(given = "CEFET/RJ", role = "cph")
  )
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
Roxygen: list(markdown = TRUE)
Depends: R (>= 4.1.0)
Imports: stats, DescTools, e1071, elmNNRcpp, FNN, forecast, hht, KFAS,
        mFilter, nnet, randomForest, wavelets, dplyr, daltoolbox
Config/roxygen2/version: 8.0.0
RoxygenNote: 8.0.0
Config/pak/sysreqs: cmake make libicu-dev libuv1-dev libssl-dev
        libx11-dev zlib1g-dev
Repository: https://cefet-rj-dal.r-universe.dev
Date/Publication: 2026-05-22 16:29:23 UTC
RemoteUrl: https://github.com/cefet-rj-dal/tspredit
RemoteRef: HEAD
RemoteSha: 83874f341eb19efc3888d6c740c62a58e09d3e6c
NeedsCompilation: no
Packaged: 2026-06-21 10:47:04 UTC; root
Author: Eduardo Ogasawara [aut, ths, cre] (ORCID:
    <https://orcid.org/0000-0002-0466-0626>),
  Cristiane Gea [aut],
  Diego Carvalho [ctb],
  Diogo Santos [aut],
  Arthur Garcia [aut],
  Eduardo Bezerra [ctb],
  Esther Pacitti [ctb],
  Fabio Porto [ctb],
  Fernando Alexandrino [aut],
  Rebecca Salles [aut],
  Vitoria Birindiba [aut],
  CEFET/RJ [cph]
Maintainer: Eduardo Ogasawara <eogasawara@ieee.org>