Titre :

Short-term air temperature forecasting using Nonparametric Functional Data Analysis and SARMA models

Auteur(s) :

Dabo-Niang, Sophie [Auteur]
Lille économie management - UMR 9221 [LEM]
MOdel for Data Analysis and Learning [MODAL]
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]

Titre de la revue :

Environmental Modelling and Software

Pagination :

394-408

Éditeur :

Elsevier

Date de publication :

2019-01

ISSN :

1364-8152

Mot(s)-clé(s) en anglais :

SARMA
Time series
Air temperature
Forecasting
Functional data analysis

Discipline(s) HAL :

Mathématiques [math]/Statistiques [math.ST]

Résumé en anglais : [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 ...
Lire la suite >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.Lire moins >

Langue :

Anglais

Comité de lecture :

Oui

Audience :

Internationale

Vulgarisation :

Non

Collections :

• Lille Économie Management (LEM) - UMR 9221

Source :

Harvested from HAL