A freight inspection volume forecasting approach using an aggregation/disaggregation procedure, machine learning and ensemble models

Abstract

Machine learning methods are a powerful tool to detect workload peaks and congestion in goods inspec- tion facilities of seaports. In this paper, a time series data of freight inspection volume at the Border Inspections Posts in the Port of Algeciras Bay was used to construct 4 datasets based on different sizes of autoregressive window and several machine learning and ensemble models were used to aid decision- making in the inspection process. Moreover, an aggregation/disaggregation procedure to make predictions was proposed and compared to two different prediction horizons: daily ( t + 1 ) and weekly ( t + 7 ) pre- dictions. In general, results showed that neural networks performed better than any other model inde- pendently of the size of the autoregressive window. The result obtained by a weighted average ensemble model was better and statistically significant than any other model. Moreover, the proposed aggrega- tion/disaggregation procedure provided better performance results and more robust in terms of variance than considering daily or weekly predictions.