Port Throughput Time Series Prediction Based on Dynamic Integration Algorithm
QU Li-l · Jisuanji fangzhen · 2014
It is usually difficult for the prediction effect of local port cargo throughput forecasting model to obtain precise prediction result. A combination forecasting model was established to adjust weights dynamically. According to the error of single model,Bayesian conditional posterior probability was used to set single model weights in integrated prediction. By predicting performance comparative evaluation index,the proposed algorithm has a better prediction accuracy,which will provide the basis for accurate prediction of port throughput.