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Updated: May 16, 2025

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预测船舶估值的新方法:应用于最高级和超高级二手市场
Elif Tuçe Bal1, Ercan Akan2, Huseyin Gencer3
1Department of Maritime Business Administration, Iskenderun Technical University, Hatay, Turkey.
PloS one
|May 14, 2025
概括
机器学习 (ML) 模型可以提高船级/超级级船舶的船舶估值准确度. 使用销售月之前的历史波罗的海交易所指数的简化模型,特别是XGBoost,提供了优越和可靠的预测.
科学领域:
- 海洋经济学 海洋经济学
- 数据科学数据科学数据科学
- 金融建模金融建模
背景情况:
- 准确的船舶估值对于船舶销售,购买和海事保险至关重要.
- 传统的估值方法往往缺乏动态市场所需的精度.
- 机器学习 (ML) 算法显示出优越预测性能的潜力.
研究的目的:
- 使用 ML. 开发一个高度准确的船舶估值模型,用于supramax/ultramax细分市场.
- 为了比较基于线性回归模型的各种ML算法的预测精度.
- 创建一个简化,市场可访问的模型,用于船舶估值的实际应用.
主要方法:
- 开发了两种使用历史数据的线性回归模型 (2005年8月至2022年12月) 对supramax/ultramax船只.
- 独立变量包括出售月份和前几个月的波罗的海交易所指数.
- 应用线性回归,决策树,随机森林和XGBoost算法用于价格预测和模型验证.
主要成果:
- 使用易于获取的市场变量进行简化的模型显示,预测性能与初始模型相比较.
- 纳入到2023年的数据进一步提高了简化模型的准确性和可靠性.
- 利用销售月前几个月的波罗的海交易所指数的模型显著优于其他模型,XGBoost被确定为表现最好的算法.
结论:
- 基于历史波罗的海交易所指数的简化ML模型可以提供准确可靠的船舶估值.
- XGBoost算法在预测supramax/ultramax船舶价值方面表现出卓越的性能.
- 拟议的简化模型为海事行业的利益相关者提供了一个实用和有效的工具.
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