Predictability of second-hand bulk carriers with a novel hybrid algorithm

Duru, Okan; Gulay, Emrah; Girgin, Sinem Celik

doi:10.1016/j.ajsl.2021.07.002

Cited by 6 publications

(1 citation statement)

References 22 publications

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“…The generalization ability of ANN provides conditions for its potential in prediction. ANN cannot only be applied in shipping economy market [13,15,21,31,32,40], but also provide help for ship technological design. Study in [5], respectively, uses ANN to estimate engine power and fuel consumption, and then estimate carbon dioxide emissions, for the recent tankers, bulk carriers and container ships built from 2015 to present.…”

Section: Literature Reviewmentioning

confidence: 99%

Forecasting Secondhand Tanker Price Through Wavelet Neural Networks Based on Adaptive Genetic Algorithm

2023

ITC

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Seaborne crude oil remains the main source of energy in the modern world in terms of volume, accounting for nearly half of all internationally traded crude oil. The shipping market is already characterized by high volatility, coupled with the impact of COVID-19 lockdown and geopolitics events. Price forecasting has become a necessary and challenging task for shipowners and other stakeholders. In the shipping market forecasting literature, the usual focus is on the newbuilding ship price or freight rate. A limited number of literature is for secondhand tanker price. On the other hand, there is few literature that use wavelet neural networks based on adaptive genetic algorithm (AGA-WNN) to predict shipping market. This paper mainly studies the application of the hybrid model to secondhand price prediction of 5 kinds of tanker sizes. The performance of AGA-WNN on time series of 10 and 15 years is compared with the basic performance provided by the six benchmark models, using three error metrics and two statistical tests. We can point out that AGA-WNN provides encouraging and promising results, outperforming the baseline models in both accuracy and robustness. It can be said that AGA-WNN gives the best overall predictive performance.

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