基于时空深度学习的股票市场预测
Yung-Chen Li1, Hsiao-Yun Huang1, Nan-Ping Yang2
1Department of Statistics and Information Science, Fu Jen Catholic University, New Taipei City 242062, Taiwan.
Entropy (Basel, Switzerland)
|September 28, 2023
概括
本研究介绍了用于股票价格预测的时空变压器模型,通过一种新的时空机制来增强变压器架构. 这种方法通过考虑空间和时间库存的相互作用来提高预测的准确性.
科学领域:
- 金融预测 财务预测
- 机器学习用于金融.
- 时间序列分析时间序列分析.
背景情况:
- 像LSTM和变压器这样的传统模型很难将空间信息纳入股票价格预测中.
- 准确的股价预测对于投资策略和市场分析至关重要.
研究的目的:
- 介绍时空制造者模型,一种用于股票价格预测的新方法.
- 评估将时空机制纳入财务预测中的有效性.
- 将时空变压器的性能与现有的LSTM和变压器模型进行比较.
主要方法:
- 开发了时空变压器模型,将时空机制集成到变压器架构中.
- 利用台湾50指数组成部分的十分钟股票价格数据和台湾证券交易所的日内数据.
- 使用多个时间步骤的股票价格数据和每日移动窗口训练模型.
主要成果:
- 与LSTM和变压器模型相比,空间时间变压器模型在股票价格预测方面表现优异.
- 该模型成功地捕获了基本的股价趋势变化,并提供了稳定的预测.
- 时间空间机制被证明是重要的和有价值的提高预测准确性.
结论:
- 时空模拟器模型通过有效整合空间和时间数据,在股票价格预测方面取得了重大进展.
- 拟议的方法为寻求提高投资回报率的投资者提供了有价值的工具.
- 该研究强调了时空机制在为金融市场开发更准确的预测模型方面的重要性.
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