Forecasting container throughput of Qingdao port with a hybrid model

Abstract

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.