Wavelet Space Partitioning for Symbolic Time Series Analysis

作     者：Venkatesh Rajagopalan Asok Ray 

作者机构：Department of Mechanical and Nuclear Engineering College of Engineering The Pennsylvania State University University Park PA 16802-1412 USA 

出 版 物：《Chinese Physics Letters》 (中国物理快报（英文版）)

年 卷 期：2006年第23卷第7期

页      面：1951-1954页

核心收录：

学科分类：07[理学] 0805[工学-材料科学与工程（可授工学、理学学位）] 070201[理学-理论物理] 0704[理学-天文学] 0702[理学-物理学] 

基　　金：Supported in Part by the U.S Aumy Research Laboratory the U.S. Army Office 

主　　题：ANOMALY DETECTION 

摘      要：A crucial step in symbolic time series analysis （STSA） of observed data is symbol sequence generation that relies on partitioning the phase-space of the underlying dynamical system. We present a novel partitioning method, called wavelet-space （WS） partitioning, as an alternative to symbolic false nearest neighbour （SFNN） partitioning. While the WS and SFNN partitioning methods have been demonstrated to yield comparable performance for anomaly detection on laboratory apparatuses, computation of WS partitioning is several orders of magnitude faster than that of the SFNN partitioning.