Materiales
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.
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Cita BibTeX
@article{AlvarezLiebanaRuizMedina19,
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 = {https://link.springer.com/article/10.1007/s00477-019-01712-z},
year = {2019}
}