A surrogate model approximates the outputs of a Partial Differential Equations (PDEs) solver with a low computational cost. In this article, we propose a method to build learning-based surrogates in the context of parameterized PDEs, which are PDEs that depend on a set of parameters but are also temporal and spatial processes. Our contribution is a method hybridizing the Proper Orthogonal Decomposition and several Support Vector Regression machines. We present promising results on a first electromagnetic use case (a primitive single-phase transformer).