FDTD time series extrapolation by the least squares support vector machine method with the particle swarm optimization technique

A new combination of particle swarm optimization (PSO) and least-squares support vector machines (LS-SVM) technique for FDTD time series forecasting is presented. In this paper, the PSO is extended to optimize the hyperparameter used in the LS-SVM algorithm. Numerical simulations demonstrate that the PSO method can efficiently get the optimal value of the hyperparameter used in the LS-SVM algorithm. And the PSO_LS-SVM method can improve the computational efficiency of the FDTD algorithm when compared with the direct FDTD method.