Forecasting performance of cruise passengers: the Spanish ports case

Abstract

This contribution examines the passenger forecasting performance of the SARIMA method applied to cruise activities in the main Spanish ports. In this port system, the cruise activity market is characterized by different seasonal patterns (i.e., once-or twice-yearly peaks, which means unimodal or bimodal behavior) due to repositioning strategy. The outcome of standard indicators for accuracy testing reveals inconsistent prediction performances among ports. These inconsistencies are analyzed using an index of bimodality and seasonal variability. The forecasted values for a high-level of bimodality and seasonal variability show worse prediction performances than unimodal patterns and low seasonal variability. Ports with less passenger activity entail larger predictions errors. Exponential and linear models were adjusted between the error metrics and the mentioned indexes.

Is entropy an indicator of port traffic predictability? The evidence from Chinese ports

“…Container tra c prediction is a complex and dynamic process (Geng et al, 2015) for which several methods are used. Among them, the ARIMA model is one of the most popular and widely applicable univariate models (Feng et al, 2020;Grifoll et al, 2021;Feng et al, 2019), and has been widely used in many forecasting elds. Litterman (1985) pointed out that the ARIMA model has high accuracy in shortterm prediction.…”

“…Historical time-series data of the tra c in different ports appear with diverse characteristics such as periodicity, seasonality, volatility and uncertainty (Peng and Chu, 2009;Feng et al, 2019). Meanwhile, the forecasting performance of port tra c is closely related to those characteristics (Grifoll et al, 2021), i.e., a less complex data set suggests higher forecasting performance, or in other words, a more complex data set is usually accompanied by poorer prediction results. In this sense, the forecasting performance is relevant to the complexity of the time-series data.…”

Tourism demand with subtle seasonality: Recognition and forecasting

“…The Seasonal naïve model and the exponential smoothing (ETS) model are generally used as benchmark models (Athanasopoulos et al, 2011; Önder and Gunter, 2016). The seasonal autoregressive integrated moving average (ARIMA) model is the most widely used for tourism seasonal prediction, demonstrating superiority (Goh and Law, 2002; Song et al, 2019; Grifoll et al, 2021). In addition, versions of the basic structural model (Turner and Witt, 2001) and seasonal fractional model (George Assaf et al, 2011) are also applied to process seasonality.…”

Hybrid approaches for container traffic forecasting in the context of anomalous events: The case of the Yangtze River Delta region in the COVID-19 pandemic

“…Accurate forecasting can also help strategic management and policy development by allowing better real-time decision-making ( Stavroulakis and Papadimitriou, 2017 ), especially in the context of anomalous events such as the COVID-19 pandemic. In addition, port authorities can use forecasting methods for route optimisation, resources assignment and terminal management ( Grifoll, 2019 ; Grifoll et al, 2021 ; Tsai and Huang, 2017 ; Levine et al, 2009 ).…”

“…The Autoregressive Integrated Moving Average (ARIMA) model is the most extensive and useful approach for container throughput forecasting; it is convenient and efficient in computation and outperforms other models in some cases, especially in short-term forecasting ( Geng et al, 2015 ). The ARIMA model is also successfully applied in many other fields of forecasting, such as economic, traffic and environmental problems ( Grifoll et al, 2021 ; Nepal et al, 2020 ). The ARIMAX model is based on the ARIMA model, where ‘X’ stands for “exogenous” external information, which can improve forecasting performance.…”