使用特征子集优化预测比特币股价
Saurabh Singh1, Anil Pise2, Byungun Yoon3
1Department of Ai and big data, Woosong University, Daejeon, south korea.
Heliyon
|April 1, 2024
概括
由于市场波动,准确预测比特币价格至关重要. 分析区块细节作为具有长短期内存 (LSTM) 网络的特征,产生了最精确的比特币价格预测.
科学领域:
- 计算金融是指计算金融.
- 加密货币分析分析
- 机器学习应用 机器学习应用
背景情况:
- 比特币作为一种投资工具越来越受欢迎,因此需要准确的价格预测.
- 加密货币的市场波动凸显了对强大的预测模型的需求.
- 之前的研究已经探索了用于比特币价格预测的机器学习,但很少有人研究了各种功能子集和建模方法.
研究的目的:
- 为了确定预测比特币价格的最佳内部特征子集.
- 评估不同特征分类对预测准确性的影响.
- 加强在波动的加密货币市场中做出明智的投资决策.
主要方法:
- 将比特币的内部特征分为四组:货币数据,区块详细信息,挖矿信息和网络难度.
- 使用长短期记忆 (LSTM) 人工神经网络进行价格预测.
- 在每个特征子集的两年半的历史数据上训练LSTM模型.
主要成果:
- 在使用区块详细功能类别时,LSTM模型实现了平均绝对错误率6.38%.
- 区块详细特征与比特币价格和低模糊性有积极的相关性,有助于提高预测准确性.
- 与其他特征子集相比,区块细节提供了最准确的比特币价格预测.
结论:
- 该研究确定区块细节是使用LSTM模型预测比特币价格的最有效的特征子集.
- 这一发现为未来研究优化加密货币预测模型提供了基础.
- 准确的比特币价格预测可以帮助投资者应对市场波动.
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