Short-term air temperature forecasting ...
Document type :
Compte-rendu et recension critique d'ouvrage
Title :
Short-term air temperature forecasting using Nonparametric Functional Data Analysis and SARMA models
Author(s) :
Dabo-Niang, Sophie [Auteur]
MOdel for Data Analysis and Learning [MODAL]
Lille économie management - UMR 9221 [LEM]
Curceac, Stelian [Auteur]
Rothamsted Research
Ternynck, Camille [Auteur]
Evaluation des technologies de santé et des pratiques médicales - ULR 2694 [METRICS]
Ouarda, Taha [Auteur]
Masdar Institute of Science and Technology [Abu Dhabi]
Chebana, Fateh [Auteur]
Institut National de la Recherche Scientifique [Québec] [INRS]
MOdel for Data Analysis and Learning [MODAL]
Lille économie management - UMR 9221 [LEM]
Curceac, Stelian [Auteur]
Rothamsted Research
Ternynck, Camille [Auteur]
Evaluation des technologies de santé et des pratiques médicales - ULR 2694 [METRICS]
Ouarda, Taha [Auteur]
Masdar Institute of Science and Technology [Abu Dhabi]
Chebana, Fateh [Auteur]
Institut National de la Recherche Scientifique [Québec] [INRS]
Journal title :
Environmental Modelling and Software
Pages :
394-408
Publisher :
Elsevier
Publication date :
2019-01
ISSN :
1364-8152
English keyword(s) :
SARMA
Time series
Air temperature
Forecasting
Functional data analysis
Time series
Air temperature
Forecasting
Functional data analysis
HAL domain(s) :
Mathématiques [math]/Statistiques [math.ST]
English abstract : [en]
Air temperature is a significant meteorological variable that affects social activities and economic sectors. In this paper, a non-parametric and a parametric approach are used to forecast hourly air temperature up to 24 h ...
Show more >Air temperature is a significant meteorological variable that affects social activities and economic sectors. In this paper, a non-parametric and a parametric approach are used to forecast hourly air temperature up to 24 h in advance. The former is a regression model in the Functional Data Analysis framework. The nonlinear regression operator is estimated using a kernel function. The smoothing parameter is obtained by a cross-validation procedure and used for the selection of the optimal number of closest curves. The other method applied is a Seasonal Autoregressive Moving Average (SARMA) model, the order of which is determined by the Bayesian Information Criterion. The obtained forecasts are combined using weights calculated based on the forecast errors. The results show that SARMA has a better performance for the first 6 forecasted hours, after which the Non-Parametric Functional Data Analysis (NPFDA) model provides superior results. Forecast pooling improves the accuracy of the forecasts.Show less >
Show more >Air temperature is a significant meteorological variable that affects social activities and economic sectors. In this paper, a non-parametric and a parametric approach are used to forecast hourly air temperature up to 24 h in advance. The former is a regression model in the Functional Data Analysis framework. The nonlinear regression operator is estimated using a kernel function. The smoothing parameter is obtained by a cross-validation procedure and used for the selection of the optimal number of closest curves. The other method applied is a Seasonal Autoregressive Moving Average (SARMA) model, the order of which is determined by the Bayesian Information Criterion. The obtained forecasts are combined using weights calculated based on the forecast errors. The results show that SARMA has a better performance for the first 6 forecasted hours, after which the Non-Parametric Functional Data Analysis (NPFDA) model provides superior results. Forecast pooling improves the accuracy of the forecasts.Show less >
Language :
Anglais
Popular science :
Non
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