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Computational Procedures for Improving Extreme Value Estimation in Time Series Modelling

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Computational Science and Its Applications – ICCSA 2023 Workshops (ICCSA 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14105))

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Abstract

In the last decades, some work has been developed in parameter estimation of extreme values jointly with time series analysis. Those results show relevant asymptotic properties. However, for finite samples, limiting results provide approximations that can be poor. Some challenges have been developed by combining Extreme Value Theory and time series modelling to obtain more reliable extreme value parameter estimates. In classical time series modelling a key issue is to determine how many parameters must be included in the model. Special care must be given to extreme events in the series that need specific statistical procedures based on the behaviour of extremes. Resampling procedures such as the jackknife and the bootstrap have been used to improve parameters estimation in Extreme Value Theory combined with time series modelling. New approaches, based on bootstrap procedures are shown and are illustrated with a real data set using the   software.

This work is funded by national funds through the FCT - Fundação para a Ciência e a Tecnologia, I.P., under the scope of the projects UIDB/00006/2020 (CEAUL), UIDB/00297/2020 and UIDP/00297/2020 (Center for Mathematics and Applications).

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Acknowledgments

The authors thank the three referees for their constructive comments and valuable suggestions, which led to substantial improvements to this work. Clara Cordeiro and Manuela Neves are partially financed by national funds through FCT - Fundação para a Ciência e a Tecnologia under the project UIDB/00006/2020 (CEAUL). This work is funded by national funds through the FCT - Fundação para a Ciência e a Tecnologia, I.P., under the scope of the projects UIDB/00297/2020 and UIDP/00297/2020 (Center for Mathematics and Applications).

Author information

Authors and Affiliations

  1. Center for Mathematics and Applications (NOVA Math) and Department of Mathematics, NOVA SST, Caparica, Portugal

    Dora Prata Gomes

  2. Faculdade de Ciências e Tecnologia, Universidade do Algarve, Faro, Portugal

    Clara Cordeiro

  3. Instituto Superior de Agronomia, Universidade de Lisboa, Lisboa, Portugal

    Manuela Neves

  4. CEAUL - Centro de Estatística e Aplicações, Faculdade de Ciências, Universidade de Lisboa, Lisboa, Portugal

    Clara Cordeiro & Manuela Neves

Authors
  1. Dora Prata Gomes
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  2. Clara Cordeiro
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  3. Manuela Neves
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Correspondence to Dora Prata Gomes .

Editor information

Editors and Affiliations

  1. University of Perugia, Perugia, Italy

    Osvaldo Gervasi

  2. University of Basilicata, Potenza, Italy

    Beniamino Murgante

  3. University of Minho, Braga, Portugal

    Ana Maria A. C. Rocha

  4. University of Cagliari, Cagliari, Italy

    Chiara Garau

  5. University of Basilicata, Potenza, Italy

    Francesco Scorza

  6. University of Massachusetts Medical School, Worcester, MA, USA

    Yeliz Karaca

  7. Polytechnic University of Bari, Bari, Italy

    Carmelo M. Torre

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Gomes, D.P., Cordeiro, C., Neves, M. (2023). Computational Procedures for Improving Extreme Value Estimation in Time Series Modelling. In: Gervasi, O., et al. Computational Science and Its Applications – ICCSA 2023 Workshops. ICCSA 2023. Lecture Notes in Computer Science, vol 14105. Springer, Cham. https://doi.org/10.1007/978-3-031-37108-0_6

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  • DOI: https://doi.org/10.1007/978-3-031-37108-0_6

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