长期短期金融时间序列预测基于剩余的多尺度TCN Sparse专家网络和通报者
概括
本研究介绍了ResMMoT-Informer,这是一种用于股票市场预测的新型深度学习模型. 它有效地捕捉了短期和长期的金融趋势,在准确性和稳定性方面表现优于现有模型.
科学领域:
- * 计算金融学
- * 机器学习 * 机器学习
- * 时间序列分析.
背景情况:
- * 传统的时间序列模型与股票市场波动性和长期预测作斗争.
- *现有的神经网络往往无法捕捉长期趋势和全球市场的依赖性.
- * 需要能够进行可靠的财务预测的先进模型.
研究的目的:
- * 提出一个新的网络架构,ResMMoT-Informer,用于增强股票市场预测.
- * 改进捕捉金融市场中多个规模的本地特征和全球依赖关系.
- * 提高短期和长期股票市场分析的预测准确性和稳定性.
主要方法:
- * 开发了ResMMoT-Informer,将剩余的多尺度时间卷积网络 (TCN) 稀疏专家网络 (ResMMoT) 与Informer架构相结合.
- * 利用剩余结构和稀疏的多尺度TCN专家网络进行稳定的培训和灵活的时间特征建模.
- * 整合了来自Informer的改进的自我注意机制,用于长序预测和波纹噪声降低 (WNR),以提高稳定性.
主要成果:
- * 废弃实验证实了拟议策略和网络结构的有效性和必要性.
- *ResMMoT-Informer在纳斯达克100数据集的长期和短期预测方面表现出卓越的表现.
- * 与现有模型相比,实现了明显更好的预测准确性和概括能力.
结论:
- *ResMMoT-Informer在股票市场预测方面提供了尖端优势,性能优于流行的模型.
- * 该模型表现出卓越的预测准确性,时间稳定性和可解释性.
- * 它有效地解决了传统模型在捕捉复杂的金融市场动态方面的局限性.
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