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

A - Papers appearing in refereed journals

Curceac, S., Ternynck, C., Ouarda, T. B. M. J, Chebana, F. and Niang, S. D. 2019. Short-term air temperature forecasting using Nonparametric Functional Data Analysis and SARMA models. Environmental Modelling & Software. 111, pp. 394-408.

AuthorsCurceac, S., Ternynck, C., Ouarda, T. B. M. J, Chebana, F. and Niang, S. D.
Abstract

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.

KeywordsFunctional data analysis; SARMA; Time series; Air temperature; Forecasting
Year of Publication2019
JournalEnvironmental Modelling & Software
Journal citation111, pp. 394-408
Digital Object Identifier (DOI)doi:10.1016/j.envsoft.2018.09.017
Open accessPublished as non-open access
FunderMasdar Institute of Science and Technology
Output statusPublished
Publication dates
Online24 Sep 2018
Publication process dates
Accepted23 Sep 2018
PublisherElsevier Sci Ltd
Copyright licensePublisher copyright
ISSN1364-8152

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