RT Journal Article
T1 Prediction of air pollutants PM10 by ARBX(1) processes
A1 Álvarez Liébana, Javier
A1 Ruiz Medina, María Dolores
A2 Langousis, Andreas
AB 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.
PB Springer Nature
YR 2019
FD 2019
LK https://hdl.handle.net/20.500.14352/95035
UL https://hdl.handle.net/20.500.14352/95035
LA eng
NO Álvarez-Liébana J, Ruiz-Medina MD (2019) Prediction of air pollutants PM10 by ARBX(1) processes. Stoch Environ Res Risk Assess 33:1721–1736. https://doi.org/10.1007/s00477-019-01712-z
NO Ministerio de Economía y Competitividad (España)
NO European Commission
DS Docta Complutense
RD 9 abr 2025