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TSPredIT: Integrated Tuning of Data Preprocessing and Time Series Prediction Models

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Transactions on Large-Scale Data- and Knowledge-Centered Systems LIV

Part of the book series: Lecture Notes in Computer Science ((TLDKS,volume 14160))

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Abstract

Prediction is one of the most important activities while working with time series. There are many alternative ways to model the time series. Finding the right one is challenging to model them. Most data-centric models (either statistical or machine learning) have hyperparameters to tune. Setting them right is mandatory for good predictions. It is even more complex since time series prediction also demands choosing a data preprocessing that complies with the chosen model. Many time series frameworks, such as Scikit Learning, have features to build models and tune their hyperparameters. However, only some works address tuning data preprocessing hyperparameters and model building. TSPredIT addresses this issue in this scope by providing a framework that seamlessly integrates data preprocessing activities with models’ hyperparameters. TSPredIT is made available as an R-package, which provides functions for defining and conducting time series prediction, including data pre(post)processing, decomposition, hyperparameter optimization, modeling, prediction, and accuracy assessment. Besides, TSPredIT is also extensible, which significantly expands the framework’s applicability, especially with other languages such as Python.

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Notes

  1. 1.

    https://github.com/cefet-rj-dal/tspredit.

  2. 2.

    https://cran.r-project.org/web/packages/daltoolbox/index.html.

  3. 3.

    http://www.fertilizer.org.

  4. 4.

    https://eic.cefet-rj.br/~dal/tspredit/.

  5. 5.

    https://cefet-rj-dal.github.io/tspredit/.

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Acknowledgements

The authors thank CNPq, CAPES (finance code 001), and FAPERJ for partially sponsoring this research.

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Authors and Affiliations

  1. Federal Center for Technological Education of Rio de Janeiro (CEFET/RJ), Rio de Janeiro, Brazil

    Rebecca Salles, Eduardo Bezerra, Celso Marques, Carla Pacheco, Carla Oliveira & Eduardo Ogasawara

  2. University of Montpellier, Montpellier, France

    Esther Pacitti

  3. National Institute for Research in Digital Science and Technology (INRIA), University of Montpellier, Montpellier, France

    Esther Pacitti & Carla Oliveira

  4. National Laboratory for Scientific Computing (LNCC), Petropolis, Brazil

    Carla Oliveira & Fabio Porto

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  1. Rebecca Salles
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  2. Esther Pacitti
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  3. Eduardo Bezerra
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  4. Celso Marques
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  5. Carla Pacheco
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  6. Carla Oliveira
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  7. Fabio Porto
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  8. Eduardo Ogasawara
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Corresponding author

Correspondence to Eduardo Ogasawara .

Editor information

Editors and Affiliations

  1. Paul Sabatier University, IRIT, Toulouse, France

    Abdelkader Hameurlain

  2. Technical University of Vienna, IFS, Vienna, Austria

    A Min Tjoa

  3. Aix-Marseille University, LIS, Marseille, France

    Omar Boucelma

  4. Université Clermont Auvergne, CNRS, LIMOS, Aubiere, France

    Farouk Toumani

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Salles, R. et al. (2023). TSPredIT: Integrated Tuning of Data Preprocessing and Time Series Prediction Models. In: Hameurlain, A., Tjoa, A.M., Boucelma, O., Toumani, F. (eds) Transactions on Large-Scale Data- and Knowledge-Centered Systems LIV. Lecture Notes in Computer Science(), vol 14160. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-68014-8_2

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  • DOI: https://doi.org/10.1007/978-3-662-68014-8_2

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