基于mfo优化GRU网络的新型混合方法预测股价

Q1 Decision Sciences
Xinjian Zhang, Guanlin Liu
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引用次数: 0

摘要

随着社会经济的快速发展和股票市场的不断发展,越来越多的人开始关注股票投资。在认知计算的工程应用领域,预测股票价值的重要性日益增加。利用数据驱动策略预测股价,投资者可以有效地降低风险,提高利润。投资者可以利用基于历史价值和文本数据的预测,对股票价格的未来模式做出明智的判断。股价预期是金融领域的一项关键工作,对交易员和投资者都有重大影响。本文对预测股票价格波动的机器学习策略进行了深入的比较分析。本研究采用了历史股票数据和多种技术指标。提出了一种基于粒子群优化(PSO)、生物地理优化(BBO)和蛾焰优化(MFO)的纳斯达克指数预测门控循环单元(GRU)模型。在这些优化器中,MFO的效果最好。与GRU方案相比,优化后的PSO-GRU、BBO-GRU和mfo -优化的GRU对股票预测的决定系数分别为0.9807、0.9824和0.9904 (\({R}^{2}\)),表明该方案的发展取得了一定的进步。用于评估该模型的标准是平均绝对误差、均方根绝对误差和\({R}^{2}\)。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Stock Prices Forecasting by Using a Novel Hybrid Method Based on the MFO-Optimized GRU Network

With the social economy growing at a quick pace and the stock market seeing constant developments, more and more people are voicing concerns about investing in stocks. The importance of forecasting stock values has increased in the domain of engineering's use of cognitive computing. Utilizing data-driven tactics for forecasting stock prices, investors can effectively mitigate risks and enhance profits. Investors can use projections based on historical values and textual data to make well-informed judgments about future patterns in stock prices. Stock price anticipation is a pivotal undertaking in the financial sector that has substantial consequences for traders and investors. This article presents an in-depth comparison analysis of machine learning tactics for forecasting price fluctuations in stocks. The research deploys historical stock data and diverse technical indicators. This paper presents the Gated Recurrent Unit (GRU) model for Nasdaq stock index anticipation, which is optimized by Particle swarm optimization (PSO), Biogeography-based optimization (BBO), and Moth flame optimization (MFO). Among these optimizers, MFO has the best outcomes. Compared to the GRU scheme the optimized PSO-GRU, BBO-GRU, and MFO-optimized GRU for stock forecasting has the outcomes of 0.9807, 0.9824, and 0.9904 in coefficient of determination (\({R}^{2}\)) which shows the improvement of the presented scheme as a result of its development. The criteria used to evaluate this model are mean absolute error, root mean absolute error, and \({R}^{2}\).

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来源期刊
Annals of Data Science
Annals of Data Science Decision Sciences-Statistics, Probability and Uncertainty
CiteScore
6.50
自引率
0.00%
发文量
93
期刊介绍: Annals of Data Science (ADS) publishes cutting-edge research findings, experimental results and case studies of data science. Although Data Science is regarded as an interdisciplinary field of using mathematics, statistics, databases, data mining, high-performance computing, knowledge management and virtualization to discover knowledge from Big Data, it should have its own scientific contents, such as axioms, laws and rules, which are fundamentally important for experts in different fields to explore their own interests from Big Data. ADS encourages contributors to address such challenging problems at this exchange platform. At present, how to discover knowledge from heterogeneous data under Big Data environment needs to be addressed.     ADS is a series of volumes edited by either the editorial office or guest editors. Guest editors will be responsible for call-for-papers and the review process for high-quality contributions in their volumes.
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