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首页> 外文期刊>IEEE transactions on industrial informatics >Learning Shapelet Patterns from Network-Based Time Series
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Learning Shapelet Patterns from Network-Based Time Series

机译：从基于网络的时间序列中学习小波模式

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摘要

This paper formulates the problem of learning discriminative features (i.e., segments) from networked time-series data, considering the linked information among time series. For example, social network users are considered to be social sensors that continuously generate social signals represented as a time series. The discriminative segments are often referred to as shapelets in a time series. Extracting shapelets for time-series analysis has been widely studied. However, existing works on shapelet selection assume that the time series are independent and identically distributed. This assumption restricts their applications to social networked time-series analysis since a user's actions can be correlated to his/her social affiliations. In this paper, we propose a novel network regularized least squares (NetRLS) feature selection model that combines typical time-series data and user network data for analysis. Experiments on real-world Twitter, Weibo, and DBLP networked time-series data demonstrate the performance of the proposed method. NetRLS performs better than the representative baselines on four evaluation criteria, namely classification accuracy, area under the curve (AUC), F1-score, and statistical significance analysis. NetRLS also has competitive running time as the baselines.
机译：考虑到时间序列之间的链接信息，本文提出了从网络时间序列数据中学习区分特征（即分段）的问题。例如，社交网络用户被认为是持续生成表示为时间序列的社交信号的社交传感器。区分性片段通常在时间序列中称为小波。提取小波以进行时间序列分析已被广泛研究。但是，现有的关于Shapelet选择的工作假设时间序列是独立的并且分布均匀。该假设将其应用限制在社交网络时间序列分析中，因为用户的行为可以与他/她的社交关系相关联。在本文中，我们提出了一种新颖的网络正则化最小二乘（NetRLS）特征选择模型，该模型结合了典型的时间序列数据和用户网络数据进行分析。在真实世界的Twitter，微博和DBLP网络时间序列数据上的实验证明了该方法的性能。 NetRLS在四个评估标准上的表现优于代表性基准，即分类准确性，曲线下面积（AUC），F1得分和统计显着性分析。 NetRLS还具有竞争优势的运行时间作为基准。

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