The inclusion of exogenous variables in functional autoregressive ozone forecasting

By J. Damon, Serge Guillas. 2002

121 citations recorded by the source corpus.

Subfields: Atmospheric Science, Environmental Engineering, Health, Toxicology and Mutagenesis

View Paper: https://www.semanticscholar.org/paper/5237d57e13575735e685d0a29ff140caa63c5503

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In Atmospheric Science

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Citations in the corpus

Conditional Quantiles for Dependent Functional Data with Application to the Climatic El Niño Phenomenon (2005, methodology): “More recently, nonparametric methods taking into account functional variables have been developed with very interesting practical motivations dealing with environmetrics (see Damon and Guillas 2002, Fernandez et al. 2003, Aneiros et al. 2004), chemometrics (see Ferraty and Vieu 2002), meteorologic”
Estimating Some Characteristics of the Conditional Distribution in Nonparametric Functional Models (2006, background): “Even if we will mainly deal in this paper with theoretical features of nonparametric functional problems, it is worth to note that there are an increasing sources of potential applications of these functional models, as well in chemiometrics (see [13]), environmetrics, (see [ 9 ,10]), meteorological”
Determining the order of the functional autoregressive model (2013, methodology): “The collection of Ferraty and Romain (2011) covers many recent developments in this field. The books of Ferraty and Vieu (2006) and Ramsay et al. (2009) focus, respectively, on nonparametric methods and computational issues.”
Forecasting Functional Time Series with a New Hilbertian ARMAX Model: Application to Electricity Price Forecasting (2018, background): “2700287 the study of demographic curves (mortality and fertility rates for each age observed every year [1]), or the study of ozone concentration over a certain period of time in different locations [2].”
Weakly dependent functional data (2010, unknown): “The Annals of Statistics 2010, Vol. 38, No. 3, 1845–1884 DOI: 10.1214/09-AOS768 © Institute of Mathematical Statistics, 2010”
Curve Forecasting by Functional Autoregression (2008, methodology): “For example, [9,3,4,2,11] study the forecasting of electricity consumption, traffic, climatic variations, electrocardiograms, and ozone concentration, respectively, using a generalization of the autoregression model to functional data.”
Functional time series approach for forecasting very short-term electricity demand (2013, methodology): “In future work, we aim to explore a functional regression model with discrete and functional types of regressors and compare its forecast accuracy with the established functional autoregressive of order 1 with exogenous variables proposed by Damon and Guillas [9].”
Locally Stationary Functional Time Series (2016, methodology): “Technical details can be found in the appendix and several auxiliary results that are of independent interest are proved in the supplementary document van Delft and Eichler (2017c), henceforth referred to as the Online Supplement.”