长短期记忆(LSTM)与量子长短期记忆(QLSTM)的比较研究:预测股市走势

Tariq Mahmood, Ibtasam Ahmad, Malik Muhammad Zeeshan Ansar, Jumanah Ahmed Darwish, Rehan Ahmad Khan Sherwani
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引用次数: 0

摘要

近年来,金融分析师一直在努力开发预测股价指数走势的模型。在巴基斯坦这样的经济、社会和政治局势下,这项任务变得极具挑战性。在这项研究中,我们采用了高效的机器学习模型,如长短时记忆(LSTM)和量子长短时记忆(QLSTM),通过 2004 年 2 月至 2020 年 12 月期间 26 个经济、社会、政治和行政指标的月度数据来预测卡拉奇证券交易所(KSE)100 指数。LSTM 和 QLSTM 预测的 KSE 100 指数值与实际值的比较结果表明,QLSTM 是一种预测股市趋势的潜在技术。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Comparative Study of Long Short-Term Memory (LSTM) and Quantum Long Short-Term Memory (QLSTM): Prediction of Stock Market Movement
In recent years, financial analysts have been trying to develop models to predict the movement of a stock price index. The task becomes challenging in vague economic, social, and political situations like in Pakistan. In this study, we employed efficient models of machine learning such as long short-term memory (LSTM) and quantum long short-term memory (QLSTM) to predict the Karachi Stock Exchange (KSE) 100 index by taking monthly data of twenty-six economic, social, political, and administrative indicators from February 2004 to December 2020. The comparative results of LSTM and QLSTM predicted values of the KSE 100 index with the actual values suggested QLSTM a potential technique to predict stock market trends.
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