基于递归稀疏在线学习的时间序列拓扑识别  

TIME SERIES TOPOLOGY RECOGNITION BASED ON RECURSIVE SPARSE ONLINE LEARNING

作　　者：张志丽 古晓明[1] 王文晶 Zhang Zhili;Gu Xiaoming;Wang Wenjing(Shanxi Institute of Economic Management,Taiyuan 030024,Shanxi,China;Shanxi Vocational University of Engineering Science and Technology,Taiyuan 030000,Shanxi,China)

机构地区：[1]山西经济管理干部学院,山西太原030024 [2]山西工程科技职业大学,山西太原030000

出　　处：《计算机应用与软件》2025年第1期277-286,共10页Computer Applications and Software

基　　金：山西省教育科学规划课题(HLW-20165)。

摘　　要：针对动态时间序列之间的因果关系分析问题,提出一种基于递归稀疏在线学习的拓扑识别方法。通过复合目标迭代最小化序列稀疏拓扑识别标准,促进稀疏更新;通过基于最小二乘法的估计准则,大大提高对输入可变性的跟踪性能,并且瞬时损失函数考虑历史样本,进一步通过静态遗憾分析得到对数遗憾界限;在真实数据和合成数据上的结果表明所提出算法在静态和动态场景中的有效性。For causality analysis between dynamic time series,a topology recognition method based on recursive sparse online learning is proposed.The sequence sparse topology identification standard was minimized by composite target iteration to promote sparse update.The tracking performance of input variability was greatly improved by the estimation criterion based on the least square method,and the historical samples were considered in the instantaneous loss function.The logarithmic regret limit was obtained through static regret analysis.The numerical results of real data and synthetic data show the effectiveness of the proposed algorithm in static and dynamic scenes.

关 键 词：递归稀疏 时间序列 在线学习 拓扑识别 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程]