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Novel Mathematics Inspired by Industrial Challenges pp 271–297Cite as

Prediction Models with Functional Data for Variables related with Energy Production

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Part of the Mathematics in Industry book series (TECMI,volume 38)

Abstract

In this chapter, different dynamic regression models designed for the prediction of variables related with energy production, mainly, with variables associated with the price and the energy demand are presented.

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Author information

Authors and Affiliations

  1. Department of Statistics, Mathematical Analysis and Optimization, Universidade de Santiago de Compostela, Wuppertal, Germany

    Manuel Febrero–Bande & Wenceslao González–Manteiga

  2. Technological Institute for Industrial Mathematics and Department of Statistics, Mathematical Analysis and Optimization, Universidade de Santiago de Compostela, Wuppertal, Germany

    Manuel Oviedo de la Fuente

Authors
  1. Manuel Febrero–Bande
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  2. Wenceslao González–Manteiga
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  3. Manuel Oviedo de la Fuente
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Corresponding author

Correspondence to Manuel Febrero–Bande .

Editor information

Editors and Affiliations

  1. Applied Math. & Numerical Analysis, Bergische Universität Wuppertal, Wuppertal, Nordrhein-Westfalen, Germany

    Michael Günther

  2. Mathematics, TU Eindhoven, Eindhoven, Noord-Brabant, The Netherlands

    Wil Schilders

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© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG

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Cite this chapter

Febrero–Bande, M., González–Manteiga, W., de la Fuente, M.O. (2022). Prediction Models with Functional Data for Variables related with Energy Production. In: Günther, M., Schilders, W. (eds) Novel Mathematics Inspired by Industrial Challenges. Mathematics in Industry(), vol 38. Springer, Cham. https://doi.org/10.1007/978-3-030-96173-2_10

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