解码比特币:利用时间序列分析中的宏观和微观因素进行价格预测
Hae Sun Jung1, Jang Hyun Kim2, Haein Lee3
1Department of Applied Artificial Intelligence, Sung Kyun Kwan University, Seoul, Republic of South Korea.
PeerJ. Computer science
|September 24, 2024
概括
这项研究通过分析超过2000天的各种数据,包括技术,社会和宏观经济因素,提高了比特币价格预测. 双向长期短期记忆 (Bi-LSTM) 模型实现了高精度,为投资者和决策者提供了宝贵的见解.
科学领域:
- * 计算金融学
- * 数据科学数据科学
- * 计量经济学 计量经济学
背景情况:
- * 由于比特币的市场影响和波动性,比特币价格预测至关重要.
- *以前的研究经常使用有限的数据或特征.
- * 需要综合模型,包括各种数据,以准确预测比特币价格.
研究的目的:
- * 预测比特币价格使用时间序列分析与广泛的数据集 (>2,000天).
- * 评估用于时间序列预测的机器学习和深度学习框架.
- * 通过各种输入功能来确定最佳窗口大小并提高预测准确性.
主要方法:
- * 在超过2000天的数据集上使用时间序列分析.
- * 整合了各种不同的输入功能:技术指标,情绪分析 (社交媒体,新闻,谷歌趋势),宏观经济指标,链上数据和传统金融资产.
- * 评估了机器学习和深度学习模型,重点是双向长期短期记忆 (Bi-LSTM).
主要成果:
- *双向长期短期记忆 (Bi-LSTM) 模型表现出显著的预测性能.
- *在窗口大小为3的情况下,Bi-LSTM实现了0.01824的根平均平方误差 (RMSE),0.01213的平均绝对误差 (MAE),2.97%的平均绝对百分比误差 (MAPE) 和0.98791的R平方误差.
- *梯度重要性和剥离试验证实了各种输入特征的有效性和影响.
结论:
- * 拟议的方法有效地预测比特币价格,通过整合广泛的数据类别.
- *Bi-LSTM模型证明了强大,即使包括COVID-19流行病等异常事件.
- * 调查结果支持在加密货币领域做出明智的投资决策和制定政策.
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