A Deep Ensemble Neural Network Approach to Improve Predictions of Container Inspection Volume

Abstract

The use of predictive models at the border inspection posts in a port may help to manage and plan operations processes in such a way that time delays and congestion issues are minimized. In this paper, an enriched time series database containing records of the number of inspections carried out in the Port of Algeciras Bay between 2010 and 2018 is analyzed using two well-known statistical and computational intelligence methods such as linear regression (baseline model) and deep-fully connected neural networks. Additionally, a deep ensemble neural network approach is proposed in order to try to boost predictive performance even further. The results of the analysis show how deep fully-connected neural networks outperform a simple linear regression model, in particular the ensemble approach obtains performances of (Formula Presented) and (Formula Presented) in contrast to (Formula Presented) and (Formula Presented) achieved by linear regression. A visual comparison of the original and predicted time series shows how the ensemble approach is able to model better high and low peaks than the time series predicted by linear regression.