A transfer forecasting model for container throughput guided by discrete PSO

Accurate forecast of future container throughput of a port is very important for its construction, 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 important ports in China, Shanghai Port and Ningbo Port are used for empirical analysis, and the results show the effectiveness of the proposed model.