深度混合多元时间序列异常检测方法
首发时间:2021-09-06
摘要:时间序列异常检测是时间序列数据挖掘中的研究热点问题,在网络安全、健康医疗、风险管理等实际应用领域占据重要地位。异常检测任务中的时间序列往往是多元高维数据且异常样本分布占比较低,基于统计或传统机器学习的方法缺乏对类不平衡问题的考虑,以及数据间顺序关系、局部特征的有效处理,导致误判或漏判异常现象的发生。本文提出一种采用深度自编码器(Deep AutoEncoder, DAE)与传统分类器(如支持向量机等)相结合的方式进行时间序列异常检测的方法。深度自编码器由卷积神经网络(Convolutional Neural Network, CNN)和长短期记忆(Long Short-Term Memory, LSTM)网络搭建,得益于CNN和LSTM能有效捕捉时间序列的局部空间特征和数据之间的相关性,原始数据在隐层子空间中的特征表示明显减弱了正常样本和异常样本之间的关系,在此基础上传统分类器更容易精准区分出异常,同时降低阈值筛选等检测方案面临的不确定性问题。通过与多个基准方法的对比实验表明,本文提出的算法在多个数据集上都表现出良好的效果。
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Deep Hybrid Multivariate Time Series Anomaly Detection
Abstract:Time series anomaly detection is a hot-topic in time series data mining, and it occupies an important position in practical applications, such as network security, health care, and risk management. The time series in anomaly detection tasks are often multivariate high-dimensional data, and the number of anomalous samples is relatively less. Methods based on statistics and traditional machine learning cannot effectively deal with the problem of class imbalances, and they ignore the order of the data and local information, leading to misjudgment and omission of anomaly. This paper proposes a method for time series anomaly detection using a combination of Deep AutoEncoder (DAE) and traditional classifiers (such as support vector machines). The DAE is built by Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM).Thanks to the ability of CNN and LSTM to effectively capture the local spatial and temporal features,the correlation of the original data in the hidden layer subspace has significantly weakened the relationship between normal samples and abnormal samples. On this basis, traditional classifiers are easier to accurately distinguish abnormalities, and decrease the uncertain of detection schemes such as threshold screening. Experiments show that compared with various baselines, the proposed method shows better results on multiple datasets.
Keywords: Time Series Anomaly Detection LSTM CNN AotuEncoder
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