A Dynamic Weighted RBF-Based Ensemble for Prediction of Time Series Data from Nuclear Components

Jie Liu (1) , Valeria Vitelli (2) , Enrico Zio (3) , Redouane Seraoui (4)

1 Ecole Centrale Paris
2 Department of Biostatistics
3 SSEC - Chaire Sciences des Systèmes et Défis Energétiques EDF/ECP/Supélec
4 EDF R&D - EDF R&D

In this paper, an ensemble approach is proposed for prediction of time series data based on a Support Vector Regression (SVR) algorithm with RBF loss function. We propose a strategy to build diverse sub-models of the ensemble based on the Feature Vector Selection (FVS) method of Baudat & Anouar (2003), which decreases the computational burden and keeps the generalization performance of the model. A simple but effective strategy is used to calculate the weights of each data point for different sub-models built with RBF-SVR. A real case study on a power production component is presented. Comparisons with results given by the best single SVR model and a fixed-weights ensemble prove the robustness and accuracy of the proposed ensemble approach.

Domaines: Energie électrique; Statistiques [stat]; Sciences de l'ingénieur [physics]

Jie Liu, Valeria Vitelli, Enrico Zio, Redouane Seraoui. A Dynamic Weighted RBF-Based Ensemble for Prediction of Time Series Data from Nuclear Components. International Journal of Prognostics and Health Management, 2015, pp.9. ⟨hal-01176328⟩