相关实验视频
评估机器学习模型,以预测加密货币价格预测的预测准确度
Shavez Mushtaq Qureshi1, Atif Saeed2, Farooq Ahmad2
1Department of Computer Science, Qarshi University, Lahore, Pakistan.
PeerJ. Computer science
|October 31, 2025
概括
机器学习模型,如随机森林和梯度提升,显示出在波动的加密货币市场中对算法交易的强有力的预测性能. 解决数据不平衡对于开发强大和利的加密货币交易策略至关重要.
科学领域:
- 计算金融是一种计算金融.
- 机器学习应用程序 机器学习应用程序
- 加密货币市场分析分析
背景情况:
- 全球加密货币日益普及,需要强大的交易模式.
- 在波动性加密货币市场的算法交易带来了独特的挑战和机遇.
- 可靠的预测模型对于加密货币投资的知情决策至关重要.
研究的目的:
- 研究用于算法交易的机器学习分类模型的预测性能和稳定性.
- 为了比较各种模型,包括后勤回归,随机森林和梯度增强.
- 为了确定可靠的方法,有利可图的加密货币交易策略开发.
主要方法:
- 从加密货币交易所收集和预处理的历史数据.
- 训练并评估了后勤回归,随机森林和梯度增强模型.
- 研究了类不平衡,重新采样技术和超参数调整的影响,强调后置测试.
主要成果:
- 随机森林,XGBoost和梯度增强模型始终优于其他模型.
- 解决类不平衡问题显著改善了模型性能.
- 超参数调整和现实的后台测试对于模型评估至关重要.
结论:
- 机器学习模型,特别是随机森林和梯度增强,为算法加密货币交易提供了有希望的途径.
- 未来的研究应该探索情绪分析,强化学习和深度学习,以提高策略.
- 调查结果为开发强大和利的加密货币交易策略提供了指导.
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