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PMANet:用于中国股票价格预测的时间序列预测模型

Wenke Zhu¹, Weisi Dai², Chunling Tang³

¹College of Bangor, Central South University of Forestry and Technology, Changsha, 410004, Hunan, China.

Scientific reports

|August 7, 2024

概括

本研究介绍了PMANet,这是一个用于股票价格预测的混合模型. 通过了解库存数据的相互关系和处理异常,PMANet提高了准确性,提供可靠的财务预测.

科学领域:

* 计算金融计算金融
* 金融数据科学金融数据科学
* 金融中的机器学习

背景情况:

* 股票市场预测对于交易利能力至关重要.
*现有的模型在数据相互关系,长序列和异常方面扎.
*风能平台提供了有价值的股票价格和相关数据.

研究的目的:

* 介绍PMANet,这是一个用于股票价格预测的先进混合模型.
* 提高对库存数据依赖性和相互关系的理解.
* 提高预测的准确性和稳定性,特别是在异常和长序列方面.

主要方法:

* 开发了PMANet,这是一个混合模型,集成了多尺度定时特征注意力,多尺度定时特征卷积和粒子群优化.
* 利用概率定位注意力来捕捉复杂的数据依赖关系.
*采用粒子群集优化来增强异常处理.

主要成果:

*从四个主要行业对中国股票的实证研究验证了PMANet的表现.
*PMANet在股票价格预测方面展示了可行性和多功能性.
*PMANet产生的预测与实际库存价值非常接近.

结论:

*PMANet有效地解决了以前库存预测模型的局限性.
* 该模型显示出强大的预测能力,与实际应用需求保持一致.
*PMANet为准确的金融市场预测提供了一个强大的解决方案.

关键词:

深度学习是一种深度学习.举报者是一个告密者.股票价格预测股票价格预测时间序列预测模型

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