Abstract 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.

中文翻译：

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CPM：对比度多元时间序列的通用特征依赖模式挖掘框架

摘要 随着传感器技术的最新进展，多元时间序列数据变得非常庞大，具有复杂但有见地的变量间依赖模式。在受控实验中挖掘对比依赖模式可以帮助量化控制和实验时间序列之间的差异，但是，压倒了从业者的能力。现有方法难以确定差异是由干预引起还是由不同状态引起。我们提出了一种新的对比模式挖掘 (CPM) 框架，通过多元高斯分布联合确定和表征两个时间序列中的动态状态，以找到与干预相关的差异。在 CPM 框架下，我们不仅提出了一种新的基于协方差的对比模式模型，但也整合了我们之前提出的基于部分相关的模型作为一个特例。开发了一种有效的通用算法，通过调整其中一个子程序来优化各种 CPM 模型。进行了综合实验以分析所提出框架的有效性、可扩展性、实用性和可解释性。