tspredit: Time Series Prediction with Integrated Tuning
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>.
| Version: |
1.2.747 |
| Depends: |
R (≥ 4.1.0) |
| Imports: |
stats, DescTools, e1071, elmNNRcpp, FNN, forecast, hht, KFAS, mFilter, nnet, randomForest, wavelets, dplyr, daltoolbox |
| Published: |
2025-10-27 |
| DOI: |
10.32614/CRAN.package.tspredit |
| Author: |
Eduardo Ogasawara 
[aut, ths, cre],
Cristiane Gea [aut],
Diego Carvalho [ctb],
Diogo Santos [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 at ieee.org> |
| BugReports: |
https://github.com/cefet-rj-dal/tspredit/issues |
| License: |
MIT + file LICENSE |
| URL: |
https://cefet-rj-dal.github.io/tspredit/,
https://github.com/cefet-rj-dal/tspredit |
| NeedsCompilation: |
no |
| Materials: |
README |
| CRAN checks: |
tspredit results |
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