Order-Preserving Metric Learning for Mining Multivariate Time Series
Order-Preserving Metric Learning for Mining Multivariate Time Series
复制标题
DOI：
10.1109/icdm50108.2020.00080
复制
发表时间：
2020-11
期刊：
影响因子：
--
通讯作者：
Jie Xu;Zhenxing Xu;Bin Yu;Fei Wang
中科院分区：
文献类型：
--
作者：
Jie Xu;Zhenxing Xu;Bin Yu;Fei Wang
近年来，由于基因组学研究、健康信息学、金融和异常检测等众多领域中产生的大量MTS数据，多变量时间序列（MTS）分析是一个越来越热门的研究课题。数据的特殊性使其成为一项具有挑战性的任务，例如，缺失数据、不同的采样频率和随机噪声。此外，每个实例不仅依赖于它的过去值，而且还依赖于其他实例，并存在歧视性的顺序依赖特性。为了解决这些挑战，在本文中，我们介绍了一个保序度量学习框架的多变量时间序列预测。具体来说，我们采用四重约束，其中可以包括成对和三重约束模型的相似性，从复杂的标签关系。为了保留MTS中实例的固有时间关系，将保序Wasserstein距离集成到框架中以衡量MTS数据之间的差异，其中逆差矩正则化强制流网络具有局部同质结构，而先验分布正则化的KL发散则防止流网络具有遥远的时间位置的实例之间的网络。除了流网络上的正则化，Wasserstein距离的地面测量被替换为Mahalanobis距离，以增加其识别能力。提出了一种交替迭代策略，用于联合优化地面测量中的马氏距离矩阵和Wasserstein距离流网络。大量的实验，从重症监护的真实世界的临床数据，以证明所提出的方法对脓毒症预测任务的有效性。
Multivariate time series (MTS) analysis is an increasingly popular research topic in recent years due to the vast amount of MTS data that are being generated in numerous fields such as genomics research, health informatics, finance and abnormal detection. The particularity of the data makes it a challenging task, e.g., missing data, different sampling frequencies, and random noise. Moreover, each instance depends not only on its past values but also has some dependency on other instances, and there exist discriminatory order-dependent characteristics. To address these challenges, in this paper, we introduce an order-preserving metric learning framework for multivariate time series prediction. Specifically, we adopt quadruplet-wise constraints which can encompass pair-wise and triplet-wise constraints to model similarity from complex label relations. To preserve the inherent temporal relationships of the instances in MTS, order-preserving Wasserstein distance is integrated to the framework to measure dissimilarity between MTS data, where the inverse difference moment regularization enforces flow-network with local homogeneous structures and the KL-divergence with a prior distribution regularization prevents flow-network between instances with faraway temporal locations. Besides the regularizations on flow-network, the ground measurement of the Wasserstein distance is replaced by Mahalanobis distance to increase its discrimination capability. An alternating iteration strategy is proposed to jointly optimize the Mahalanobis distance matrix in the ground measurement and the flow-network of Wasserstein distance. Extensive experiments on real-world clinical data from critical care are provided to demonstrate the effectiveness of the proposed method on sepsis prediction task.