CTFNet:基于卷积和时间频率分析的长序时间序列预测
IEEE transactions on neural networks and learning systems
|August 17, 2023
概括
新型神经网络CTFNet通过整合时间域 (TD) 和频域 (FD) 特性提取来增强长序时间序列预测 (LSTF). 这种方法显著减少了对单变量和多变量时间序列的预测错误.
科学领域:
- 人工智能的人工智能
- 机器学习 机器学习
- 时间序列分析 时间序列分析
背景情况:
- 目前用于长序列时间序列预测 (LSTF) 的最先进 (SOTA) 方法难以捕捉长期依赖关系,并且具有很高的计算复杂性.
- 现有的模型经常面临信息利用瓶,限制其在复杂的LSTF任务中的有效性.
研究的目的:
- 提出CTFNet,一种轻量级的单层隐藏传送神经网络 (SLFN),旨在克服当前LSTF方法的局限性.
- 通过卷积映射和时间频率分解的新组合,增强特征提取并降低LSTF中的计算复杂性.
主要方法:
- CTFNet采用一个时间域 (TD) 特性挖掘策略,使用矩阵分解来捕获样本点之间的长期相关性.
- 多任务频域 (FD) 功能挖掘被用来提取多种频率信息,同时最大限度地减少数据损失,整合全球和本地环境的多尺度扩展卷积.
- 该模型旨在实现高效率,具有短的训练时间和快速推断速度.
主要成果:
- 对9个基准数据集的实证研究表明,CTFNet的性能优于SOTA方法.
- 对于多变量时间序列,CTFNet的预测误差降低了64.7%,对于单变量时间序列降低了53.7%.
- 提出的方法有效地打破了数据利用瓶,并确保了特征提取的完整性.
结论:
- 通过有效地解决特征提取挑战和计算复杂性,CTFNet为LSTF提供了显著的进步.
- 该模型集成TD和FD分析的能力为准确和高效的长序列预测提供了强大的框架.
- 对于各种LSTF应用,CTFNet是一个有希望的,高效的解决方案.
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