具有异常感知的多变量时间序列异常检测模型

IF 3.5 4区 计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE PeerJ Computer Science Pub Date : 2024-07-31 DOI:10.7717/peerj-cs.2172
Dong Wei, Wu Sun, Xiaofeng Zou, Dan Ma, Huarong Xu, Panfeng Chen, Chaoshu Yang, Mei Chen, Hui Li
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引用次数: 0

摘要

多变量时间序列异常检测是一种重要的数据挖掘技术,在信息技术应用等领域有着广泛的应用。目前,由于异常标签的稀缺性,大多数时间序列数据异常检测方法都依赖于无监督方法。然而,在现实世界中,获取数量有限的异常标签是可行且经济实惠的。有效利用这些标签可以为了解异常的时间特征提供有价值的见解,并在指导异常检测工作中发挥关键作用。为了提高多元时间序列异常检测的性能,我们提出了一种名为 EDD(编码器-解码器-判别器)的新型深度学习模型,该模型可利用有限的异常样本。EDD 模型创新性地将图注意网络与长短期记忆(LSTM)整合在一起,从多元时间序列数据中提取空间和时间特征。这种集成方法使模型能够捕捉数据中的复杂模式和依赖关系。此外,该模型还能将序列数据巧妙地映射到潜在空间中,利用精心设计的损失函数将正常数据紧密聚类到潜在空间中,同时随机分散异常数据。这种创新设计使潜空间中正常数据和异常数据的概率分布截然不同,从而实现了对异常数据的精确识别。为了评估 EDD 模型的性能,我们在三个不同的数据集上进行了广泛的实验验证。结果表明,我们的模型在多变量时间序列异常检测方面具有明显的优势。具体来说,在两种评估方法中,我们的模型的平均 F1 分数分别比第二好的方法高出 2.7% 和 73.4%,凸显了其卓越的检测能力。这些发现验证了我们提出的 EDD 模型在利用有限的异常样本对多元时间序列数据进行准确、稳健的异常检测方面的有效性。
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An anomaly detection model for multivariate time series with anomaly perception
Multivariate time series anomaly detection is a crucial data mining technique with a wide range of applications in areas such as IT applications. Currently, the majority of anomaly detection methods for time series data rely on unsupervised approaches due to the rarity of anomaly labels. However, in real-world scenarios, obtaining a limited number of anomaly labels is feasible and affordable. Effective usage of these labels can offer valuable insights into the temporal characteristics of anomalies and play a pivotal role in guiding anomaly detection efforts. To improve the performance of multivariate time series anomaly detection, we proposed a novel deep learning model named EDD (Encoder-Decoder-Discriminator) that leverages limited anomaly samples. The EDD model innovatively integrates a graph attention network with long short term memory (LSTM) to extract spatial and temporal features from multivariate time series data. This integrated approach enables the model to capture complex patterns and dependencies within the data. Additionally, the model skillfully maps series data into a latent space, utilizing a carefully crafted loss function to cluster normal data tightly in the latent space while dispersing abnormal data randomly. This innovative design results in distinct probability distributions for normal and abnormal data in the latent space, enabling precise identification of anomalous data. To evaluate the performance of our EDD model, we conducted extensive experimental validation across three diverse datasets. The results demonstrate the significant superiority of our model in multivariate time series anomaly detection. Specifically, the average F1-Score of our model outperformed the second-best method by 2.7% and 73.4% in both evaluation approaches, respectively, highlighting its superior detection capabilities. These findings validate the effectiveness of our proposed EDD model in leveraging limited anomaly samples for accurate and robust anomaly detection in multivariate time series data.
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来源期刊
PeerJ Computer Science
PeerJ Computer Science Computer Science-General Computer Science
CiteScore
6.10
自引率
5.30%
发文量
332
审稿时长
10 weeks
期刊介绍: PeerJ Computer Science is the new open access journal covering all subject areas in computer science, with the backing of a prestigious advisory board and more than 300 academic editors.
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