Analysis of Time Series Novelty Detection Strategies for Synthetic and Real Data

被引：0
作者：
André Paoliello Modenesi
Antônio Pádua Braga
机构：
[1] Federal University of Minas Gerais,Department of Electronic Engineering
来源：
Neural Processing Letters | 2009年 / 30卷
关键词：
Novelty detection; Neural networks; Signal processing; Time series; Principal component analysis; Reduced coulomb energy networks; Density probability estimation;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
Novelty detection is inspired by animal behavior and can be applied to a variety of practical situations. By automatically bringing attention to anomalous events, without the need of previously defining those events, it can perform certain tasks that would otherwise require human scrutiny. This paper reviews some approaches to the problem when applied to the time series analysis problem.
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