Result: Port throughput forecasting by MARS-RSVR with chaotic simulated annealing particle swarm optimization algorithm

Port throughput forecasting is a very complex nonlinear dynamic process, prediction accuracy is influenced by uncertainty of socio-economic factors, especially by the mixed noise (singular point) produced in the collection, transfer and calculation of statistical data; consequently, it is difficult to obtain a satisfactory port throughput forecasting result. Thus, establishing an effective port throughput forecasting scheme is still a significant research issue. Since the robust v-support vector regression model (RSVR) has the ability to solve the nonlinear and mixed noise in the port throughput history data and its related socio-economic factors, this paper introduces the RSVR model to forecast port throughput. In order to search the more appropriate parameters combination for the RSVR model, considering the proposed simulated annealing particle swarm optimization (SAPSO) algorithm and the original PSO algorithm still have the drawbacks of immature convergence and is time consuming, this study presents chaotic simulated annealing particle swarm optimization(CSAPSO) algorithm to determine the parameter combination. Aiming to identify the final input vectors for RSVR model, the multivariable adaptive regression splines (MARS) is adopted to select the final input vectors from the candidate input variables. This study eventually proposes a port throughput forecasting scheme that hybridizes the RSVR, CSAPSO and MARS to obtain a more accurate forecasting result. Subsequently, this study compiles the port throughput data and the corresponding socio-economic indicators data of Shanghai as the illustrative example to evaluate the feasibility and performance of the proposed scheme. The experimental results indicate that the proposed port throughput forecasting scheme obtains better forecasting result than the six competing models in terms of forecasting error.