Abstract Accurate forecast of future container throughput of a port is very important for its con struction, upgrading, and operation management. This study proposes a transfer forecasting model guided by discrete par...Abstract Accurate forecast of future container throughput of a port is very important for its con struction, upgrading, and operation management. This study proposes a transfer forecasting model guided by discrete particle swarm optimization algorithm （TF-DPSO）. It firstly transfers some related time series in source domain to assist in modeling the target time series by transfer learning technique, and then constructs the forecasting model by a pattern matching method called analog complexing. Finally, the discrete particle swarm optimization algorithm is introduced to find the optimal match between the two important parameters in TF-DPSO. The container throughput time series of two im portant ports in China, Shanghai Port and Ningbo Port are used for empirical analysis, and the results show the effectiveness of the proposed model.展开更多
This paper proposes a hybrid forecasting method to forecast container throughput of Qingdao Port.To eliminate the influence of outliers,local outlier factor(lof) is extended to detect outliers in time series,and then ...This paper proposes a hybrid forecasting method to forecast container throughput of Qingdao Port.To eliminate the influence of outliers,local outlier factor(lof) is extended to detect outliers in time series,and then different dummy variables are constructed to capture the effect of outliers based on domain knowledge.Next,a hybrid forecasting model combining projection pursuit regression(PPR) and genetic programming(GP) algorithm is proposed.Finally,the hybrid model is applied to forecasting container throughput of Qingdao Port and the results show that the proposed method significantly outperforms ANN,SARIMA,and PPR models.展开更多
基金partly supported by the Natural Science Foundation of China under Grant Nos.71101100 and 70731160635New Teachers’Fund for Doctor Stations,Ministry of Education under Grant No.20110181120047+5 种基金Excellent Youth Fund of Sichuan University under Grant No.2013SCU04A08China Postdoctoral Science Foundation under Grant Nos.2011M500418,2012T50148 and 2013M530753Frontier and Cross-innovation Foundation of Sichuan University under Grant No.skqy201352Soft Science Foundation of Sichuan Province under Grant No.2013ZR0016Humanities and Social Sciences Youth Foundation of the Ministry of Education of China under Grant No.11YJC870028Selfdetermined Research Funds of CCNU from the Colleges’ Basic Research and Operation of MOE under Grant No.CCNU13F030
文摘Abstract Accurate forecast of future container throughput of a port is very important for its con struction, upgrading, and operation management. This study proposes a transfer forecasting model guided by discrete particle swarm optimization algorithm （TF-DPSO）. It firstly transfers some related time series in source domain to assist in modeling the target time series by transfer learning technique, and then constructs the forecasting model by a pattern matching method called analog complexing. Finally, the discrete particle swarm optimization algorithm is introduced to find the optimal match between the two important parameters in TF-DPSO. The container throughput time series of two im portant ports in China, Shanghai Port and Ningbo Port are used for empirical analysis, and the results show the effectiveness of the proposed model.
文摘This paper proposes a hybrid forecasting method to forecast container throughput of Qingdao Port.To eliminate the influence of outliers,local outlier factor(lof) is extended to detect outliers in time series,and then different dummy variables are constructed to capture the effect of outliers based on domain knowledge.Next,a hybrid forecasting model combining projection pursuit regression(PPR) and genetic programming(GP) algorithm is proposed.Finally,the hybrid model is applied to forecasting container throughput of Qingdao Port and the results show that the proposed method significantly outperforms ANN,SARIMA,and PPR models.
