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

Sophie Dabo-Niang 1, 2 Stelian Curceac 3 Camille Ternynck 4 Taha B.M.J. Ouarda 5 Fateh Chebana 6
1 MODAL - MOdel for Data Analysis and Learning
Inria Lille - Nord Europe, LPP - Laboratoire Paul Painlevé - UMR 8524, METRICS - Evaluation des technologies de santé et des pratiques médicales - ULR 2694, Polytech Lille - École polytechnique universitaire de Lille, Université de Lille, Sciences et Technologies
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.
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https://hal.inria.fr/hal-02334991
Contributor : Sophie Dabo-Niang <>
Submitted on : Monday, October 28, 2019 - 8:36:15 AM
Last modification on : Thursday, October 1, 2020 - 12:48:09 PM

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Sophie Dabo-Niang, Stelian Curceac, Camille Ternynck, Taha B.M.J. Ouarda, Fateh Chebana. Short-term air temperature forecasting using Nonparametric Functional Data Analysis and SARMA models. Environmental Modelling and Software, Elsevier, 2019, 111, pp.394-408. ⟨10.1016/j.envsoft.2018.09.017⟩. ⟨hal-02334991⟩

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