关于时间序列的图形神经网络的调查:预测,分类,推算和异常检测
IEEE transactions on pattern analysis and machine intelligence
|August 14, 2024
概括
图形神经网络 (GNN) 通过建模复杂的关系来提供先进的时间序列分析. 本调查审查了GNN用于时间序列预测,分类,异常检测和归算,指导未来的研究和应用.
科学领域:
- 数据科学数据科学数据科学
- 机器学习 机器学习
- 人工智能的人工智能
背景情况:
- 时间序列数据对于理解由物理和虚拟传感器生成的动态系统至关重要.
- 传统的方法在时间序列数据中难以捕捉复杂的时间间和变量间关系.
- 图形神经网络 (GNN) 的最新进展显示了对增强时间序列分析的前景.
研究的目的:
- 为时间序列分析 (GNN4TS) 应用的 GNN 提供全面的审查.
- 引导研究人员和从业人员理解,应用和推进GNN4TS.
- 巩固现有知识,并突出GNN4TS的未来研究方向.
主要方法:
- 介绍了GNN4TS的以任务为导向的分类,涵盖预测,分类,异常检测和归算.
- 讨论了GNN4TS的代表性研究工作和主流应用.
- 该调查综合了基础,实际应用和未来的机会.
主要成果:
- 在时间序列数据中,GNN擅长模拟显式的时间间和变量间关系.
- 该审查涵盖了包括预测,分类,异常检测和归算在内的关键应用.
- 提供GNN4TS研究的结构化概述,确定当前的趋势和差距.
结论:
- 在时间序列分析能力方面,GNN是显著的进步.
- 这项调查是GNN4TS领域的基础资源.
- 未来的研究应该探索新的GNN架构和应用程序来解决复杂的时间序列问题.
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