Wavelet-based space partitioning for symbolic time series analysis

被引：0
|
作者：
Rajagopalan, Venkatesh [1 ]
Ray, Asok [1 ]
机构：
[1] Penn State Univ, University Pk, PA 16802 USA
来源：
2005 44th IEEE Conference on Decision and Control & European Control Conference, Vols 1-8 | 2005年
关键词：
symbolic time series analysis; wavelets; fault detection; complex systems;
D O I：
暂无
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
Recent literature has reported symbolic time series analysis of complex systems for real-time anomaly detection. A crucial aspect in this analysis is symbol sequence generation from the observed time series data. This paper presents a wavelet-based partitioning, instead of the currently practiced method of phase-space partitioning, for symbol generation. The partitioning algorithm makes use of the maximum entropy method. The wavelet-space and phase-space partitioning methods are compared with regard to anomaly detection using experimental data.
引用
收藏
页码：5245 / 5250
页数：6
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