A MULTIVARIATE TIME SERIES CLASSIFICATION METHOD FOR STREAMING DATA USING TEMPORAL METAFEATURE ABSTRACTIONS

被引：3

作者：

Sipes, Tamara ^{[1
,2
]}

Balac, Natasha ^{[3
]}

Karimabadi, Homa ^{[1
,2
]}

Wolter, Nicole ^{[3
]}

Nunes, Kenneth ^{[3
]}

Roberts, Aaron ^{[4
]}

机构：

[1] Univ Calif San Diego, ECE Dept, La Jolla, CA 92093 USA

[2] SciberQuest Inc, Del Mar, CA 92014 USA

[3] Univ Calif San Diego, San Diego Supercomp Ctr, La Jolla, CA 92093 USA

[4] NASA, Goddard Space Flight Ctr, Greenbelt, MD 20771 USA

来源：

INTERNATIONAL JOURNAL OF SEMANTIC COMPUTING | 2013年 / 7卷 / 02期

基金：

美国国家科学基金会;

关键词：

Multivariate time series classification; temporal data mining; metafeature abstraction;

D O I：

10.1142/S1793351X13400084

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In this paper we demonstrate a new approach to the classification of multivariate time series streaming data by utilizing a temporal metafeature abstractions method. The technique extracts global features and metafeatures in order to capture the necessary time-lapse information in the streams of data. The features are then used to create a static, intermediate stream representation that includes all the important time-varying information, and is suitable for analysis using the standard supervised data mining techniques. The capability of the new algorithm called MineTool-TS2 was demonstrated through its application to three datasets: UCSD Microgrid energy usage data, a space physics dataset and synthetic data.

引用

页码：173 / 183

页数：11

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