Prediction of air pollutants PM10 by ARBX(1) processes

ARBX(1) waves,
Forecasting PM10 haze,
Air pollution craze.
Air pollutants
Banach spaces
Functional data
Functional time series
Weather data

Javier Álvarez Liébana, María Dolores Ruiz Medina, «Prediction of air pollutants PM10 by ARBX(1) processes», Stoch. Environ. Res. Risk Assess. 33, 1721-1736 (2019), doi: 10.1007/s00477-019-01712-z


Universidad Complutense de Madrid

Universidad de Granada


August 2019




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.

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Cita BibTeX

  author = {J. Álvarez-Liébana and M. D. Ruiz-Medina},
  title = {Prediction of air pollutants PM10 by ARBX(1) processes},
  journal = {Stochastic Environmental Research and Risk Assessment},
  volume = {33},
  pages = {1721-1736},
  keywords = {air pollutants forecasting, Banach spaces, functional time series, meteorological variables, strong consistency},
  doi = {10.1007/s00477-019-01712-z},
  url = {},
  year = {2019}