Application of support vector machine and least squares vector machine to freight volume forecast

Aiming at features of strong randomicity, complexity and nonlinearity in highway freight volume, two forecasting models based on support vector machine (SVM) and least squares support vector machine (LSSVM) are proposed. Comparative research and numerical calculation on these two models shows that the forecasting precise based on SVM is better than LSSVM's, and computational speed of the latter is smaller than the first one. The two methods are both high precise forecasting and are satisfied with the engineering requirement. The forecasting model based on LSSVM is efficient for the freight volume forecasting.