SVM Forecasting Model for Water Resources Based on Chaotic State Space Reconstruction

Aim at the nonlinear forecasting of water resources time series, a SVM forecasting model based on chaotic state space reconstruction is established. The original time series is reconstructed to a high characteristic dimension space through nonlinear mapping so as to gain the input vector and anticipant output vector. The SVM model based on statistical learning theory is chosen for the forecasting. By example study, the SVM model has such advantages as better curve fitting, higher precision and stronger generalization.