使用混合卷积循环模型架构的比特币价格预测

IF 1.5 0 ENGINEERING, MULTIDISCIPLINARY
Omar M. Ahmed, Lailan M. Haji, Ayah M. Ahmed, Nashwan M. Salih
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引用次数: 0

摘要

金融领域广泛使用实时预测股票价格的工具,这是在创建预测过程中使用的工具。在本文中,我们试图以一种既准确又可靠的方式预测比特币的价格。与更传统的方法相反,深度学习模型被用于管理大量数据并生成预测。本研究的目的是开发一种使用混合卷积循环模型(HCRM)架构预测股票价格的方法。该模型架构集成了两个独立的深度学习模型的优势:一维卷积神经网络(1D-CNN)和长短期记忆(LSTM)。1D-CNN负责特征提取,LSTM负责时间回归。所建立的1D-CNN-LSTM模型在股票价值预测方面表现优异。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Bitcoin Price Prediction using the Hybrid Convolutional Recurrent Model Architecture
The field of finance makes extensive use of real-time prediction of stock price tools, which are instruments that are put to use in the process of creating predictions. In this article, we attempt to predict the price of Bitcoin in a manner that is both accurate and reliable. Deep learning models, as opposed to more traditional methods, are used to manage enormous volumes of data and to generate predictions. The purpose of this research is to develop a method for predicting stock prices using the Hybrid Convolutional Recurrent Model (HCRM) architecture. This model architecture integrates the advantages of two separate deep learning models: The 1-Dimensional-Convolusional Neural Network (1D-CNN) and the Long-Short Term Memory (LSTM). The 1D-CNN is responsible for the feature extraction, while the LSTM is in charge of the temporal regression. The developed 1D-CNN-LSTM model has an outstanding performance in predicting stock values.
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来源期刊
Engineering, Technology & Applied Science Research
Engineering, Technology & Applied Science Research ENGINEERING, MULTIDISCIPLINARY-
CiteScore
3.00
自引率
46.70%
发文量
222
审稿时长
11 weeks
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