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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>.

Versions across snapshots

VersionRepositoryFileSize
1.2.767 rolling linux/jammy R-4.5 tspredit_1.2.767.tar.gz 335.6 KiB
1.2.767 rolling linux/noble R-4.5 tspredit_1.2.767.tar.gz 335.5 KiB
1.2.767 rolling source/ R- tspredit_1.2.767.tar.gz 147.2 KiB
1.2.767 latest linux/jammy R-4.5 tspredit_1.2.767.tar.gz 335.6 KiB
1.2.767 latest linux/noble R-4.5 tspredit_1.2.767.tar.gz 335.5 KiB
1.2.767 latest source/ R- tspredit_1.2.767.tar.gz 147.2 KiB
1.2.767 2026-04-26 source/ R- tspredit_1.2.767.tar.gz 147.2 KiB
1.2.767 2026-04-23 source/ R- tspredit_1.2.767.tar.gz 147.2 KiB
1.2.767 2026-04-09 windows/windows R-4.5 tspredit_1.2.767.zip 344.1 KiB
1.0.787 2025-04-20 source/ R- tspredit_1.0.787.tar.gz 35.5 KiB

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