• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文会议>IEEE International Conference on Acoustics, Speech and Signal Processing >Training LSTM for Unsupervised Anomaly Detection Without A Priori Knowledge
【24h】

Training LSTM for Unsupervised Anomaly Detection Without A Priori Knowledge

机译：在没有先验知识的情况下训练LSTM进行无监督异常检测

获取原文
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Unsupervised anomaly detection on time-series is widespread in the industry and an active research topic. Recently, impressive results have been obtained by leveraging the progresses of deep learning, and in particular through the use of Long Short Term Memory (LSTM) neural networks. Yet, latest state-of-the-art unsupervised LSTM-based solutions still require a priori knowledge about normality as they need to train the model on time-series without any anomaly. In contrast, we propose a novel anomaly detector, coined as LSTM-Decomposed (LSTM-D), that does not require this normality knowledge. More specifically, we pre-process the timeseries with a spectral based information reduction such that the LSTM-based detector receiving the time-series becomes less likely to learn the anomaly, and hence miss its detection. We motivate our intuitions through simple examples and verify the performance improvement with respect to state-of-the-art solutions in a reference and publicly available data set.
机译：在时间序列上进行无监督的异常检测在业界非常普遍，并且是一个活跃的研究主题。最近，通过利用深度学习的进展，尤其是通过使用长期短期记忆（LSTM）神经网络，已经获得了令人印象深刻的结果。但是，最新的基于LSTM的无监督最新解决方案仍需要先验性的正态性，因为它们需要在时间序列上训练模型而没有任何异常。相反，我们提出了一种新颖的异常检测器，称为LSTM-Decomposed（LSTM-D），不需要这种正态性知识。更具体地说，我们使用基于频谱的信息约简对时间序列进行预处理，以使接收到该时间序列的基于LSTM的检测器变得不太可能学习到异常，因此错过了对其的检测。我们通过简单的示例激发我们的直觉，并在参考数据和可公开获得的数据集中验证有关最新解决方案的性能改进。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号