Similarity-Based Searching in Multi-Parameter Time Series Databases.

Lh Lehman, M Saeed, Gb Moody, Rg Mark
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引用次数: 0

Abstract

We present a similarity-based searching and pattern matching algorithm that identifies time series data with similar temporal dynamics in large-scale, multi-parameter databases. We represent time series segments by feature vectors that reflect the dynamical patterns of single and multi-dimensional physiological time series. Features include regression slopes at varying time scales, maximum transient changes, auto-correlation coefficients of individual signals, and cross correlations among multiple signals. We model the dynamical patterns with a Gaussian mixture model (GMM) learned with the Expectation Maximization algorithm, and compute similarity between segments as Mahalanobis distances. We evaluate the use of our algorithm in three applications: search-by-example based data retrieval, event classification, and forecasting, using synthetic and real physiologic time series from a variety of sources.

多参数时间序列数据库中基于相似度的搜索。
我们提出了一种基于相似性的搜索和模式匹配算法,该算法可以识别大规模多参数数据库中具有相似时间动态的时间序列数据。我们用反映单维度和多维生理时间序列动态模式的特征向量来表示时间序列片段。特征包括不同时间尺度下的回归斜率、最大瞬态变化、单个信号的自相关系数以及多个信号之间的相互关系。我们使用期望最大化算法学习的高斯混合模型(GMM)对动态模式进行建模,并以马氏距离计算路段之间的相似度。我们评估了我们的算法在三个应用中的使用:基于实例搜索的数据检索,事件分类和预测,使用来自各种来源的合成和真实生理时间序列。
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