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With the new opportunities emerging from the current wave of digitalization, terminal planning and management need to be revisited by taking a data-driven perspective. Business analytics, as a practice of extracting insights from operational data, assists in reducing uncertainties using predictions and helps to identify and understand causes of inefficiencies, disruptions, and anomalies in intra- and inter-organizational terminal operations. Despite the growing complexity of data within and around container terminals, a lack of data-driven approaches in the context of container terminals can be identified. In this chapter, the concept of business analytics for supporting terminal planning and management is introduced. The chapter specifically focuses on data mining approaches and provides a comprehensive overview on applications in container terminals and related research. As such, we aim to establish a data-driven perspective on terminal planning and management, complementing the traditional optimization perspective., Comment: 20 pages, 5 figures, book chapter

This paper discusses and empirically evaluates alternative methodologies in modeling functional outliers for high frequency time series forecasting. In spite of several modeling and forecasting methodologies that have been proposed, there have been limited advancements in monitoring and automatically identifying outlying patterns and even less in modeling those for such times series. This is a significant gap considering the difficulty and the cost associated with manual exploration and treatment of such data, due to the vast number of observations. This study proposes and assesses the performance of different modeling methodologies focusing on two key aspects, the accuracy that the outliers are modeled and the impact of each methodology on modeling normal observations. The evaluated methodologies model functional outliers using binary, integer or trigonometric dummy variables, outlier profiles or isolate them into new time series and forecast them separately. Neural networks are employed to produce the forecasts, taking advantage of their flexible nature to accommodate the different methodologies and their superior performance in high frequency time series forecasting. Hourly electricity load data from the UK are used to empirically evaluate the performance of the different methodologies.

This paper discusses and empirically evaluates alternative methodologies in modeling functional outliers for high frequency time series forecasting. In spite of several modeling and forecasting methodologies that have been proposed, there have been limited advancements in monitoring and automatically identifying outlying patterns and even less in modeling those for such times series. This is a significant gap considering the difficulty and the cost associated with manual exploration and treatment of such data, due to the vast number of observations. This study proposes and assesses the performance of different modeling methodologies focusing on two key aspects, the accuracy that the outliers are modeled and the impact of each methodology on modeling normal observations. The evaluated methodologies model functional outliers using binary, integer or trigonometric dummy variables, outlier profiles or isolate them into new time series and forecast them separately. Neural networks are employed to produce the forecasts, taking advantage of their flexible nature to accommodate the different methodologies and their superior performance in high frequency time series forecasting. Hourly electricity load data from the UK are used to empirically evaluate the performance of the different methodologies.

Neural Networks (NN) have demonstrated remarkable time series fitting and prediction abilities, outperforming in several applications other methods and particularly linear models, such as dynamic linear regression. However, due to their nature, NNs are not easy to interpret and are often considered as black box models. The importance of each independent variable is hard to estimate and therefore test whether they have significant explanatory power and hence be included in the model or not. This task is very important for several applications, where the effect of each variable has to be identified, such as marketing modelling and analysis, where the effectiveness of different marketing instruments has to be estimated, commonly modelled as impulse interventions. Statistical inference in these cases is sought, hindering the use of NNs. This paper proposes a framework to allow statistical inference of impulse interventions modelled with NNs. The effects of interventions are estimated and tested for statistical significance. Using a Monte Carlo simulation the power of the proposed test is compared with dynamic linear regression models. The power is found to be higher and the estimation of the simulated effects is more accurate. Based on this framework strategies to code multiple impulses with NNs are discussed.

Neural Networks (NN) have demonstrated remarkable time series fitting and prediction abilities, outperforming in several applications other methods and particularly linear models, such as dynamic linear regression. However, due to their nature, NNs are not easy to interpret and are often considered as black box models. The importance of each independent variable is hard to estimate and therefore test whether they have significant explanatory power and hence be included in the model or not. This task is very important for several applications, where the effect of each variable has to be identified, such as marketing modelling and analysis, where the effectiveness of different marketing instruments has to be estimated, commonly modelled as impulse interventions. Statistical inference in these cases is sought, hindering the use of NNs. This paper proposes a framework to allow statistical inference of impulse interventions modelled with NNs. The effects of interventions are estimated and tested for statistical significance. Using a Monte Carlo simulation the power of the proposed test is compared with dynamic linear regression models. The power is found to be higher and the estimation of the simulated effects is more accurate. Based on this framework strategies to code multiple impulses with NNs are discussed.

Research in forecasting with Neural Networks (NN) has provided contradictory evidence on their ability to model seasonal time series. Several empirical studies have concluded that time series should be deseasonalised prior to modelling, with contradictory evidence pointing to adequate selection of input vectors. However, the nature of seasonality itself has not been considered: econometric theory suggests that deterministic seasonality and stochastic seasonality need to be modelled differently, with only the latter requiring deseasonalisation. As prior research has failed to take the conditions of the underlying seasonality into consideration, this study explores how deterministic seasonality should be best modelled with NN to achieve accurate and robust forecasts. We consider different forms of modelling seasonality as autoregressive lags, through different encodings of explanatory variables and deseasonalisation. We evaluate the results regarding empirical accuracy and the parsimony of the input vector in order to limit the degrees of freedom, develop robust models and simplify the training of NN. Our findings are consistent with econometric literature, i.e. that no deseasonalisation is required for deterministic seasonality, and contributes to current research in NNs by identifying a more parsimonious coding of seasonality based on seasonal indices.

Research in forecasting with Neural Networks (NN) has provided contradictory evidence on their ability to model seasonal time series. Several empirical studies have concluded that time series should be deseasonalised prior to modelling, with contradictory evidence pointing to adequate selection of input vectors. However, the nature of seasonality itself has not been considered: econometric theory suggests that deterministic seasonality and stochastic seasonality need to be modelled differently, with only the latter requiring deseasonalisation. As prior research has failed to take the conditions of the underlying seasonality into consideration, this study explores how deterministic seasonality should be best modelled with NN to achieve accurate and robust forecasts. We consider different forms of modelling seasonality as autoregressive lags, through different encodings of explanatory variables and deseasonalisation. We evaluate the results regarding empirical accuracy and the parsimony of the input vector in order to limit the degrees of freedom, develop robust models and simplify the training of NN. Our findings are consistent with econometric literature, i.e. that no deseasonalisation is required for deterministic seasonality, and contributes to current research in NNs by identifying a more parsimonious coding of seasonality based on seasonal indices.