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>.
Package details |
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Author | Eduardo Ogasawara [aut, ths, cre] (ORCID: <https://orcid.org/0000-0002-0466-0626>), Fernando Alexandrino [aut], Cristiane Gea [aut], Diogo Santos [aut], Rebecca Salles [aut], Vitoria Birindiba [aut], Carla Pacheco [ctb], Eduardo Bezerra [ctb], Esther Pacitti [ctb], Fabio Porto [ctb], Diego Carvalho [ctb], CEFET/RJ [cph] |
Maintainer | Eduardo Ogasawara <eogasawara@ieee.org> |
License | MIT + file LICENSE |
Version | 1.2.727 |
URL | https://cefet-rj-dal.github.io/tspredit/ https://github.com/cefet-rj-dal/tspredit |
Package repository | View on CRAN |
Installation |
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