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Modèles semi-paramétriques appliqués à la prévision des séries temporelles. Cas de la consommation d'électricité.

Abstract : Réseau de Transport d'Electricité (RTE), in charge of operating the French electric transportation grid, needs an accurate forecast of the power consumption in order to operate it correctly. The forecasts used everyday result from a model combining a nonlinear parametric regression and a SARIMA model. In order to obtain an adaptive forecasting model, non-parametric forecasting methods have already been tested without real success. In particular, it is known that a non-parametric predictor behaves badly with a great number of explanatory variables, what is commonly called the curse of dimensionality. Recently, semi-parametric methods which improve the pure not-parametric approach have been proposed to estimate a regression function. Based on the concept of ' index', one those methods (called MAVE :Moving Average conditional- Variance Estimate) can apply to the time series. We study empirically its effectiveness to predict the future values of an autoregression time series. We then adapt this method, from a practical point of view, to predict power consumption. We propose a semi-linear semi-parametric model, partially based on the MAVE method, which allows to take into account simultaneously the autoregressive aspect of the problem and the exogenous variables. The proposed estimation procedure is practicaly efficient.
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Contributor : Vincent Lefieux <>
Submitted on : Tuesday, October 16, 2007 - 7:18:59 PM
Last modification on : Friday, March 23, 2018 - 2:04:32 PM
Long-term archiving on: : Monday, September 24, 2012 - 1:36:49 PM


  • HAL Id : tel-00179866, version 1


Vincent Lefieux. Modèles semi-paramétriques appliqués à la prévision des séries temporelles. Cas de la consommation d'électricité.. Mathématiques [math]. Université Rennes 2, 2007. Français. ⟨tel-00179866⟩



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