A prediction fusion method for reconstructing spatial temporal dynamics using support vector machines

In this paper, we propose a new spatial temporal predictor using support vector machine (SVM) and data fusion technique. SVMs are used as temporal predictors at different spatial domains and spatial temporal prediction is achieved by prediction fusion. Our proposed prediction fusion technique improves the prediction accuracy even in a non-Gaussian environment. The performance of the proposed spatial temporal predictor is analyzed. Based on real-life radar data, the proposed spatial temporal approach is shown to provide a more accurate model for sea-clutter data than the conventional methods.