Abstract
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 nuclear 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.
Highlights
Combining various data-driven approaches into an ensemble has become a popular direction of research in the last decades, motivated by the aim of improving the robustness and accuracy of the final prediction
Feature Vector Selection In Baudat and Anouar (2003), the authors propose a Feature Vector Selection (FVS) method to select a subset of the training data points (i.e. Feature Vectors (FVs)), which can represent the dimension of the whole dataset in Reproducing Kernel Hilbert Space (RKHS)
Combining Sub-Models Outputs Figure 4 shows the paradigm of DW-Radial Basis loss Function (RBF)-Ensemble, where N is the number of sub-models, x(t) is a new input vector arriving at time t, wj(t) is the weight assigned to the j-th sub-model for the new input vector, ŷj(t) is the predicted value for the j-th sub-model given by RBF-Support Vector Regression (SVR) and ŷ(t) is the final output of the ensemble model
Summary
Combining various data-driven approaches into an ensemble has become a popular direction of research in the last decades, motivated by the aim of improving the robustness and accuracy of the final prediction. An ensemble of SVRs with RBF and dynamic weighting strategy is proposed in this paper. The strategy to form the training dataset of each sub-model is based on the angle between different data points in the Reproducing Kernel Hilbert Space (RKHS), so as to reduce the computational burden. In order to be able to build ensembles of SVRs on very large datasets, FVS is used to select a smaller subset of the training data points of each sub-model, again to decrease the computational burden. All the above novel strategies are tested on the case study concerning the prediction of leak flow of the RCP in a NPP.
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