Section: New Results

Curves classification, denoising and forecasting

Participant : Jean-Michel Poggi.

In collaboration with Farouk Mhamdi and Meriem Jaidane (ENIT, Tunis, Tunisia), Jean-Michel Poggi proposed, in [18] . a method for trend extraction from seasonal time series through the Empirical Mode Decomposition (EMD). Experimental comparison of trend extraction based on EMD, X11, X12 and Hodrick Prescott filter are conducted. First results show the eligibility of the blind EMD trend extraction method. Tunisian real peak load is also used to illustrate the extraction of the intrinsic trend.

In collaboration with Mina Aminghafari (Amirkabir University, Teheran), Jean-Michel Poggi made uses of wavelets in a statistical forecasting purpose for time series. Recent approaches involve wavelet decompositions in order to handle non stationary time series. They study and extended an approach proposed by Renaud et al., to estimate the prediction equation by direct regression of the process on the Haar non-decimated wavelet coefficients depending on its past values. The new variants are used first for stationary data and after for stationary data contaminated by a deterministic trend [3] .

Jean-Michel Poggi was the supervisor (with A. Antoniadis) of the PhD Thesis of Jairo Cugliari-Duhalde which takes place in a CIFRE convention with EDF. It is strongly related to the use of wavelets together with curves clustering in order to perform accurate load comsumption forecasting. The thesis develops methodological and applied aspects linked to the electrical context as well as theoretical ones by introducing exogeneous variables in the context of nonparametric forecasting time series (see [27] and [45] ).