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Novelty Detection in Time Series Through Self-Organizing Networks: An Empirical Evaluation of Two Different Paradigms

机译：通过自组织网络的时间序列新颖性检测：两种不同范式的实证评价

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This paper addresses the issue of novelty or anomaly detection in time series data. The problem may be interpreted as a spatio-temporal classification procedure where current time series observation is labeled as normal or novel/abnormal according to a decision rule. In this work, the construction of the decision rules is formulated by means of two different self-organizing neural network (SONN) paradigms: one builds decision thresholds from quantization errors and the other one from prediction errors. Simulations with synthetic and real-world data show the feasibility of the two approaches.

机译：本文涉及时间序列数据中的新奇或异常检测问题。该问题可以被解释为时空分类过程，其中当前时间序列观察根据决定规则标记为正常或新颖/异常。在这项工作中，通过两个不同的自组织神经网络（SONN）范式制定决策规则的构建：一个从量化误差和另一个从预测错误构建判定阈值。用综合和真实世界的模拟显示了两种方法的可行性。

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来源
《Brazilian Symposium on Neural Networks》|2008年||共6页
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Aguayo Leonardo; Barreto Guilherme A.;
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中图分类 TP183-53;
关键词
Novelty detection; operator map; prediction errors; quantization errors; self-organizing map;

机译：新奇检测;操作员地图;预测误差;量化误差;自组织地图;

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1. Novelty Detection in Time Series Using Self-Organizing Neural Networks: A Comprehensive Evaluation [J] . Aguayo Leonardo, Barreto Guilherme A. Neural processing letters . 2018,第2期

机译：使用自组织神经网络的时间序列中的新颖性检测：综合评估
2. Time Series Prediction of Tropical Storm Trajectory Using Self-Organizing Incremental Neural Networks and Error Evaluation [J] . Wonjik Kim, Osamu Hasegawa Journal of Advanced Computatioanl Intelligence and Intelligent Informatics . 2018,第4a131期

机译：采用自组织增量神经网络热带风暴轨迹的时间序列预测及误差评估
3. Detecting novelties in time series through neural networks forecasting with robust confidence intervals [J] . Adriano L.I. Oliveira, Silvio R.L. Meira Neurocomputing . 2006,第1a3期

机译：通过具有鲁棒置信区间的神经网络预测来检测时间序列中的新颖性
4. Novelty Detection in Time Series Through Self-Organizing Networks: An Empirical Evaluation of Two Different Paradigms [C] . Aguayo Leonardo, Barreto Guilherme A. Brazilian Symposium on Neural Networks . 2008

机译：通过自组织网络的时间序列新颖性检测：两种不同范式的实证评价
5. Novelty detection and cluster analysis in time series data using variational autoencoder feature maps. [D] . Clachar, Sophine. 2016

机译：使用变分自动编码器特征图的时间序列数据中的新颖性检测和聚类分析。
6. Predictive time-series modeling using artificial neural networks for Linac beam symmetry: an empirical study [O] . Qiongge Li, Maria F. Chan -1

机译：直线加速器对称性的人工神经网络预测时间序列建模：一项实证研究
7. Detection of Anomalies and Novelties in Time Series with Self-Organizing Networks [O] . Aguayo Leonardo, Barreto Guilherme A. 2007

机译：利用自组织网络检测时间序列中的异常和新颖性

Novelty Detection in Time Series Through Self-Organizing Networks: An Empirical Evaluation of Two Different Paradigms

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