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Prediction of air pollutants PM10 by ARBX(1) processes

  • J. Álvarez-Liébana
  • M. D. Ruiz-MedinaEmail author
Original Paper
  • 164 Downloads

Abstract

This work adopts a Banach-valued time series framework for component-wise estimation and prediction, from temporal correlated functional data, in presence of exogenous variables. The strong-consistency of the proposed functional estimator and associated plug-in predictor is formulated. The simulation study undertaken illustrates their large-sample size properties. Air pollutants PM10 curve forecasting, in the Haute-Normandie region (France), is addressed by implementation of the functional time series approach presented.

Keywords

Air pollutants forecasting Banach spaces Functional time series Meteorological variables Strong consistency 

Notes

Acknowledgements

This work was supported in part by projects MTM2015–71839–P and PGC2018-099549-B-I00 (co-funded by Feder funds), of the DGI, MINECO, Spain.

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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Statistics, O.R. and Didactics of MathematicsUniversity of OviedoOviedoSpain
  2. 2.Department of Statistics and O.R.University of GranadaGranadaSpain

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