Time Series Prediction with Integrated Tuning R package tspredit version 2.0.707 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>. Software Depends: R (>= 4.1.0) Imports: stats, DescTools, e1071, elmNNRcpp, FNN, forecast, hht, KFAS, mFilter, nnet, randomForest, wavelets, dplyr, daltoolbox Eduardo Ogasawara <eogasawara@ieee.org> Comprehensive R Archive Network (CRAN) 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] MIT + file LICENSE (https://CRAN.R-project.org/package=tspredit/LICENSE) 2026-05-22 application/tgz https://CRAN.R-project.org/package=tspredit doi:10.32614/CRAN.package.tspredit