混合ANFIS-MPA和FFNN-MPA模型用于比特币价格预测
Ceren Baştemur Kaya1, Ebubekir Kaya2,3, Eyüp Sıramkaya2
1Department of Computer Technologies, Nevşehir Vocational School, Nevşehir Hacı Bektaş Veli University, Nevşehir 50100, Türkiye.
Biomimetics (Basel, Switzerland)
|December 24, 2025
概括
这项研究引入了使用海洋捕食者算法 (MPA) 的混合预测模型,与自适应神经模糊推理系统 (ANFIS) 和前神经网络 (FFNN) 进行比特币价格预测,显示出更好的准确性和稳定性.
科学领域:
- 计算智能是一种计算智能.
- 金融预测 财务预测
- 时间序列分析时间序列分析.
背景情况:
- 准确的短期比特币价格预测仍然具有挑战性.
- 现有的预测模型经常与加密货币市场的波动性作斗争.
- 优化算法对于训练复杂的预测模型至关重要.
研究的目的:
- 开发和评估用于短期比特币价格预测的混合预测模型.
- 将海洋捕食者算法 (MPA) 与自适应神经模糊推理系统 (ANFIS) 和前神经网络 (FFNN) 集成.
- 将MPA增强模型的性能与其他元启发算法进行比较.
主要方法:
- 利用了2022年的每日比特币价格数据.
- 数据被转化为各种输入配置的监督时间序列结构.
- 两个混合型号ANFIS-MPA和FFNN-MPA被开发和测试.
- 对六种已建立的元启发式训练算法进行了绩效评估.
主要成果:
- 海洋环境表现优越,预测错误较少,并实现更快的融合.
- 混合ANFIS-MPA和FFNN-MPA模型始终超过基线算法.
- 模型在不同复杂度的模型中显示出可靠的性能,以及具有低差异的强大,可重复的结果.
结论:
- 海洋捕食者算法是金融时间序列预测中神经模糊和神经网络模型的有效优化器.
- 集成MPA的混合模型为比特币价格预测提供了增强的预测准确性和稳定性.
- 拟议的ANFIS-MPA和FFNN-MPA方法代表了金融预测技术的重大进步.
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