Generative Anomaly Detection for Time Series Datasets
Generative Anomaly Detection for Time Series Datasets
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DOI：
10.48550/arxiv.2206.14597
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发表时间：
2022-06
期刊：
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通讯作者：
Zhuangwei Kang;Ayan Mukhopadhyay;A. Gokhale;Shijie Wen;Abhishek Dubey
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作者：
Zhuangwei Kang;Ayan Mukhopadhyay;A. Gokhale;Shijie Wen;Abhishek Dubey
交通拥堵异常检测在智能交通系统中具有十分重要的意义。交通机构的目标有两个：监测感兴趣地区的一般交通状况，并在异常拥堵状态下定位路段。对城市道路的拥堵模式建模可以达到这些目标，这相当于学习多变量时间序列(MTS)的分布。然而，现有的工作要么不具有可扩展性，要么不能同时捕获MTS中的时空信息。为此，我们提出了一个原则性和综合性的框架，该框架包括一个数据驱动的生成式方法，该方法可以执行易于处理的密度估计来检测交通异常。我们的方法首先对特征空间中的片段进行聚类，然后使用条件归一化流程在无监督环境下识别聚类级别的异常时间快照。然后，我们通过在异常簇上使用核密度估计器来识别段级别的异常。在人工数据集上的大量实验表明，我们的方法在召回率和F1-Score方面明显优于几种最先进的拥塞异常检测和诊断方法。我们还使用产生式模型对标记数据进行采样，该模型可以在有监督的环境下训练分类器，缓解了稀疏环境下异常检测缺乏标记数据的问题。
Traffic congestion anomaly detection is of paramount importance in intelligent traffic systems. The goals of transportation agencies are two-fold: to monitor the general traffic conditions in the area of interest and to locate road segments under abnormal congestion states. Modeling congestion patterns can achieve these goals for citywide roadways, which amounts to learning the distribution of multivariate time series (MTS). However, existing works are either not scalable or unable to capture the spatial-temporal information in MTS simultaneously. To this end, we propose a principled and comprehensive framework consisting of a data-driven generative approach that can perform tractable density estimation for detecting traffic anomalies. Our approach first clusters segments in the feature space and then uses conditional normalizing flow to identify anomalous temporal snapshots at the cluster level in an unsupervised setting. Then, we identify anomalies at the segment level by using a kernel density estimator on the anomalous cluster. Extensive experiments on synthetic datasets show that our approach significantly outperforms several state-of-the-art congestion anomaly detection and diagnosis methods in terms of Recall and F1-Score. We also use the generative model to sample labeled data, which can train classifiers in a supervised setting, alleviating the lack of labeled data for anomaly detection in sparse settings.