Elevator traffic flow prediction with least squares support vector machines

Elevator traffic flow is fundamental in elevator group control systems. Accurate elevator traffic flow prediction is crucial to the planning and dispatching of elevator group control systems. Support vector machine (SVM) based on statistical learning theory has shown its advantage in regression and prediction. In this paper, we predict elevator traffic flow using least squares support vector machines (LS-SVMs), which is a kind of SVM with quadratic loss function. Since SVM has greater generalization ability and guarantee global minima for given training data, it is believed that we can get good performance for elevator traffic flow with time series prediction. By using LS-SVMs, we built up three elevator traffic flow time series predictors. Experimental results show that the prediction of LS-SVMs get satisfied performance. The proposed elevator traffic flow time series prediction method is of considerable practical value and can be used in other application fields.