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

IF 2 4区 计算机科学 Q3 AUTOMATION & CONTROL SYSTEMS
Xing Ma
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引用次数: 0

Abstract

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.
基于自适应遗传算法的小波神经网络二手油轮价格预测
海运原油在数量上仍然是现代世界的主要能源来源,占所有国际贸易原油的近一半。航运市场的特点已经是高度波动,再加上2019冠状病毒病封锁和地缘政治事件的影响。对于船东和其他利益相关者来说,价格预测已经成为一项必要且具有挑战性的任务。在航运市场预测文献中,通常关注的是新造船价格或运价。数量有限的文献以二手油轮价格出售。另一方面,利用基于自适应遗传算法(AGA-WNN)的小波神经网络进行航运市场预测的文献很少。本文主要研究了混合模型在5种型号油轮二手价格预测中的应用。利用3个误差指标和2个统计检验,将AGA-WNN在10年和15年时间序列上的性能与6个基准模型提供的基本性能进行了比较。我们可以指出,AGA-WNN提供了令人鼓舞和有希望的结果,在准确性和鲁棒性方面都优于基线模型。可以说,AGA-WNN给出了最好的整体预测性能。
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来源期刊
Information Technology and Control
Information Technology and Control 工程技术-计算机:人工智能
CiteScore
2.70
自引率
9.10%
发文量
36
审稿时长
12 months
期刊介绍: Periodical journal covers a wide field of computer science and control systems related problems including: -Software and hardware engineering; -Management systems engineering; -Information systems and databases; -Embedded systems; -Physical systems modelling and application; -Computer networks and cloud computing; -Data visualization; -Human-computer interface; -Computer graphics, visual analytics, and multimedia systems.
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