分析基于深度学习的库存预测中的关键步骤:文献综述
Zinnet Duygu Akşehir1, Erdal Kılıç1
1Computer Engineering, Ondokuz Mayis University Samsun, Samsun, Turkey.
PeerJ. Computer science
|December 9, 2024
概括
深度学习模型为股票预测提供了潜力,但成功取决于数据处理和模型选择等关键步骤. 本综述通过分析准确的金融市场预测的关键要素来指导未来的研究.
科学领域:
- 金融市场和计算智能.
- 机器学习在计量经济学中的应用.
背景情况:
- 由于市场不确定性和动态条件,股票市场预测面临挑战.
- 传统的分析方法与金融市场固有的不可预测性作斗争.
- 深度学习模型越来越多地被探索,以提高股票预测准确度.
研究的目的:
- 系统地审查基于深度学习的股票预测模型.
- 调查关键实施步骤对预测绩效的影响.
- 确定研究缺口,并为未来金融预测研究提供指导.
主要方法:
- 在三个数据库中进行系统的文献搜索,用于从2020年到2024年的研究.
- 对有影响力的研究进行分析,重点关注模型开发的七个关键步骤.
- 在表格中总结研究结果,并详细识别文献差距.
主要成果:
- 深度学习模型看起来有前途,但需要仔细执行关键步骤.
- 审查确定了数据处理,特征工程和模型选择方面的具体挑战和最佳实践.
- 关键步骤显著影响库存预测模型的准确性和可靠性.
结论:
- 对数据收集,特征工程和模型选择的系统方法对于有效的基于深度学习的库存预测至关重要.
- 本次审查强调了需要改进的领域,并为开发更强大的财务预测模型提供了路线图.
- 进一步的研究应侧重于解决识别的文献差距,以推进算法交易和金融分析领域.
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