DiffMixer:一种基于混合不同频率特征的预测模型
Shengcai Zhang1, Huiju Yi1, Fanchang Zeng1
1Cyberspace Security Institute, Gansu University of Political Science and Law, Lanzhou, 730000, Gansu, China.
概括
DiffMixer通过分析和预测不同的频率来增强非静态数据的时间序列预测. 这种新的方法显著提高了对现有方法的预测准确性.
科学领域:
- 数据科学数据科学数据科学
- 机器学习 机器学习
- 信号处理 信号处理
背景情况:
- 时间序列预测对于能源和网络安全至关重要.
- 现有的变压器和MLP模型与非静止的现实世界序列作斗争.
- 对于非静止性的传统方法要么删除有用的模式,要么不充分捕捉它们.
研究的目的:
- 提出DiffMixer,一种用于分析和预测非静止时间序列中不同频率的新方法.
- 克服现有模型在处理复杂的时间依赖性和非静止性方面的局限性.
- 提高时间序列预测的准确性和稳定性.
主要方法:
- 变化模式分解 (VMD) 来提取频率组件.
- 多尺度分解 (Multi-scale Decomposition,MsD) 用于优化下采样序列的分解.
- 改进了恒星聚合再分配 (iSTAR),频域处理块 (FPB) 和双维融合 (DuDF) 进行组件间分析和融合.
主要成果:
- DiffMixer 显示预测错误的显著减少.
- 平均平方误差 (MSE) 减少了24.5%.
- 平均绝对误差 (MAE),根平均平方误差 (RMSE) 和对称平均绝对百分比误差 (SMAPE) 分别减少了12.3%,13.5%和6.1%.
结论:
- DiffMixer有效地分析和预测非静止时间序列中的不同频率.
- 拟议的方法在预测准确性方面优于最先进的技术.
- 这种方法为现实世界时间序列预测挑战提供了强大的解决方案.
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