使用具有多个输入和输出的深度神经网络预测航空客运和市场份额
Nahid Jafari1, Martin Lewison1
1School of Business, Department of Management, SUNY-Farmingdale, Farmingdale, NY, United States.
Frontiers in artificial intelligence
|October 25, 2024
概括
本研究介绍了一种两阶段的深度学习算法,用于准确的航空旅客需求预测. 该方法利用相关时间序列之间的相互依赖性来改善美国商业航空业的预测.
科学领域:
- 数据科学数据科学数据科学
- 机器学习 机器学习
- 计量经济学 计量经济学
背景情况:
- 美国商业航空业是一个重要的经济驱动力,产生数万亿美元和支持数百万个就业机会.
- 准确预测航空旅客需求对于行业规划和经济稳定至关重要.
- 在2023年,超过10亿名乘客通过美国机场旅行,突出显示了市场的规模.
研究的目的:
- 开发和介绍一种新的深度神经网络框架,用于准确地预测航空旅客需求的时间序列.
- 通过利用它们的相互依赖来应对预测相互关联的时间序列的挑战.
- 为航空业预测的现有知识体系提供独特的方法.
主要方法:
- 为相关时间序列预测开发了一种两阶段深度神经网络算法.
- 第1阶段涉及四个循环神经网络 (RNN) 模型用于单变量预测,Gated Recurrent Unit (GRU) 的表现最好.
- 第二阶段将GRU模型应用于分散的竞争对手数据,并使用连接将输入张量合并.
主要成果:
- 该研究成功开发了一个深度神经网络框架,用于预测航空旅客需求.
- 这种两阶段的方法有效地利用了相关时间序列之间的相互依赖性,以提高预测的准确性.
- 门式循环单位 (GRU) 模型在数据集内的单变量预测中表现出卓越的表现.
结论:
- 拟议的深度神经网络框架为预测美国商业航空部门的航空乘客需求提供了一种复杂的方法.
- 利用相关的时间序列及其相互依赖,可以显著提高预测的准确性.
- 该方法在预测其他复杂,相互关联的市场动态方面具有更广泛的应用潜力.
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