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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: | 1.1.707 |
Depends: | R (≥ 4.1.0) |
Imports: | dplyr, stats, forecast, mFilter, DescTools, hht, wavelets, KFAS, daltoolbox |
Published: | 2025-04-24 |
DOI: | 10.32614/CRAN.package.tspredit |
Author: | Eduardo Ogasawara |
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 |
Reference manual: | tspredit.pdf |
Package source: | tspredit_1.1.707.tar.gz |
Windows binaries: | r-devel: tspredit_1.0.787.zip, r-release: tspredit_1.1.707.zip, r-oldrel: tspredit_1.1.707.zip |
macOS binaries: | r-release (arm64): tspredit_1.1.707.tgz, r-oldrel (arm64): tspredit_1.1.707.tgz, r-release (x86_64): tspredit_1.1.707.tgz, r-oldrel (x86_64): tspredit_1.1.707.tgz |
Old sources: | tspredit archive |
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These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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