Prediction of air pollutants PM10 by ARBX(1) processes

J. Álvarez Liébana, M. D. Ruiz-Medina

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 strongconsistency of the proposed functional estimator and associated plugin 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.

Palabras clave: Air pollutants forecasting, Banach spaces, functional time series, meteorological variables, strong consistency

RM-2 Premio Ramiro Melendreras
3 de septiembre de 2019  18:30
I3L1. Edificio Georgina Blanes

M. Pelegrin, E. Carrizosa, A. Marín

S. Benítez Peña, D. Romero Morales, P. Bogetoft

S. D. Chagaboina, J. A. Carrasco López, V. Suñé Socias