使用密集的时空变压器网进行跨模式的缺失时间序列归算
Xusheng Qian1, Teng Zhang1, Meng Miao1
1State Grid Jiangsu Electric Power Company Limited Marketing Service Center, Nanjing 210019, China.
Mathematical biosciences and engineering : MBE
|June 14, 2024
概括
从传感器网络中丢失时间序列数据是一个挑战. 一个新的密集的时空变压器网络 (DSTTN) 有效地归算缺失的数据,即使在完整的数据缺失场景中,也实现了最先进的性能.
科学领域:
- 数据科学数据科学数据科学
- 人工智能的人工智能
- 传感器网络 传感器网络
背景情况:
- 传感器网络经常因为采样问题或设备故障而缺失时间序列数据.
- 现有的归算方法难以准确,特别是在完全缺失数据 (CDM) 场景中.
研究的目的:
- 开发一种准确和可靠的方法来归纳缺失的时间序列数据.
- 解决当前归算技术的局限性,特别是在CDM情况下.
主要方法:
- 提出了一种使用密集的时空变压器网络 (DSTTN) 的新型交叉模式归算方法.
- DSTTN使用堆叠的时空变压器块和密集连接将空间数据嵌入时间序列数据中.
- 在端到端管道中使用交叉模式约束和图形拉普拉斯规范化来优化模型.
主要成果:
- 对于随机和非随机丢失的数据,DSTN展示了最先进的归算性能.
- 该方法在解决具有挑战性的完整数据缺失 (CDM) 问题方面尤其有效.
- 实验性比较验证了DSTN在各种基线归算模型中的优越性.
结论:
- DSTTN在时间序列数据归算方面取得了重大进展.
- 拟议的方法为传感器网络中完整的数据缺失场景提供了可行和有效的解决方案.
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