CONVEX CLUSTERING FOR AUTOCORRELATED TIME SERIES

被引：0

作者：

Revay, Max ^{[1
]}

Solo, Victor ^{[1
]}

机构：

[1] UNSW, Sch Elect Engn & Telecommun, Sydney, NSW, Australia

来源：

2022 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP) | 2022年

基金：

澳大利亚研究理事会;

关键词：

Time series clustering; convex clustering; data mining; time series analysis; unsupervised learning; NUMBER;

D O I：

10.1109/ICASSP43922.2022.9747143

中图分类号：

O42 [声学];

学科分类号：

070206 ; 082403 ;

摘要：

While clustering in general is a heavily worked area, clustering of auto-correlated time series (CATS) has received relatively little attention. Here, we develop a convex clustering algorithm suited to auto-correlated time series and compare it with a state of the art method. We find the proposed algorithm is able to more accurately identify the true clusters.

引用

页码：3313 / 3317

页数：5

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