国际贸易市场预测和决策系统:在元学习下进行多式联运数据融合
1Faculty of Social Science & Public Policy, Department of International Development, King's College London, London, United Kingdom.
PeerJ. Computer science
|September 24, 2025
概括
本研究引入了一种新的超级学习多式联运数据融合方法,用于国际贸易市场分析. 该模型显著提高了预测准确性和性能,特别是在有限的数据下,提供了竞争优势.
科学领域:
- 经济学 经济学 经济学
- 数据科学数据科学数据科学
- 机器学习 机器学习
背景情况:
- 传统的市场分析依赖于有限的单维数据,无法捕捉市场的复杂性.
- 国际贸易数据由于其复杂性和异质性而存在挑战.
- 现有的方法难以全面反映市场状况.
研究的目的:
- 开发基于meta-learning的多式联络数据融合方法,以优化特征提取和融合.
- 解决国际贸易市场分析中单维数据的局限性.
- 提高市场预测的准确性和全面性.
主要方法:
- 用于音频信号特征提取的使用的Mel-frequency cepstral系数 (MFCC).
- 集成的卷积块注意力模块 (CBAM) 用于图像数据注意力.
- 提出了一种元学习双向特征金字塔网络 (ML-BiFPN) 用于具有适应权重的多尺度特征融合.
主要成果:
- 与现有方法相比,ML-BiFPN模型显示出更高的预测性能.
- 平均准确度提高了9.37%,F1比MLP得分增加了0.0473.
- 在小样本条件下表现出显著的优势,AP改善了2.79%.
结论:
- 拟议的多式联运数据融合模型为国际贸易市场预测提供了更高的准确性.
- 提供了对市场动态的更全面的理解,以提供知情决策.
- 通过科学支持的见解,使企业能够获得竞争优势.
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