CPM: A general feature dependency pattern mining framework for contrast multivariate time series
CPM: A general feature dependency pattern mining framework for contrast multivariate time series
复制标题
CPM：用于对比多元时间序列的通用特征依赖模式挖掘框架
DOI：
10.1016/j.patcog.2020.107711
复制
发表时间：
2021
影响因子：
8
通讯作者：
Lin, Jessica
中科院分区：
文献类型：
--
作者：
Li, Qingzhe;Zhao, Liang;Lee, Yi-Ching;Sassan, Avesta;Lin, Jessica
随着传感器技术的最新进展，多变量时间序列数据变得非常大，具有复杂但有洞察力的变量间相关性模式。在对照实验中挖掘对比依赖模式可以帮助量化对照和实验时间序列之间的差异，但这会淹没实践者的能力。现有的方法难以确定差异是由干预造成的，还是由不同的国家造成的。我们提出了一种新的对比模式挖掘(CPM)框架，通过多变量高斯分布联合确定和表征两个时间序列中的动态，从而发现与干预相关的差异。在CPM框架下，我们不仅提出了一种新的基于协方差的对比度模式模型，而且整合了我们以前提出的基于偏相关的对比度模式模型作为特例。开发了一种有效的遗传算法，通过调整其中一个子程序来优化各种CPM模型。通过全面的实验分析了该框架的有效性、可扩展性、实用性和可解释性。
With recent advances in sensor technology, multivariate time series data are becoming extremely large with sophisticated but insightful inter-variable dependency patterns. Mining contrast dependency patterns in controlled experiments can help quantify the differences between control and experimental time series, however, overwhelms practitioners’ capability. Existing methods suffer from determining whether the differences are caused by the intervention or by different states. We propose a novel Contrast Pattern Mining (CPM) framework to find the intervention-related differences by jointly determining and characterizing the dynamic states in both time series via multivariate Gaussian distributions. Under the CPM framework, we not only propose a new covariance-based contrast pattern model, but also integrate our previous proposed partial correlation-based model as a special case. An efficient generic algorithm is developed to optimize various CPM models by adjusting one of the sub-routines. Comprehensive experiments are conducted to analyze the effectiveness, scalability, utility, and interpretability of the proposed framework.
影响因子：
1.5
作者：
Lee YC;Ward McIntosh C;Winston F;Power T;Huang P;Ontañón S;Gonzalez A
通讯作者：
Gonzalez A
用于受控实验的对比特征依赖模式挖掘及其在驾驶行为中的应用
DOI：
10.1109/icdm.2019.00146
发表时间：
2019
期刊：
19th IEEE International Conference on Data Mining (ICDM
影响因子：
--
作者：
Li, Qingzhe;Zhao, Liang;Lee, Yi-Ching;Ye, Yanfang;Lin, Jessica;Wu, Lingfei
通讯作者：
Wu, Lingfei