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  • PP: Unsupervised anomaly detection via variational auto-encoder for seasonal KPIs in web applications

    Problem: unsupervised anomaly detection

    for seasonal KPIs in web applications. 

    Donut: an unsupervised anomaly detection algorithm based on VAE.

    Background:

    有的time series data have seasonal patterns occurring at regular intervals. 

    Data: KPI shapes: seasonal patterns and local variations, noises.  

    "abnormal": anomalies and missing points; detect missing points is straightforward.

    Existing methods suffer from: 这里面简直是胡说八道。

    1. the hassle麻烦 of algorithm picking
    2. parameter tuning
    3. heavy reliance on labels
    4. unsatisfying performance
    5. lack of theoretical foundations

    Methodology:

    VAE is not a sequential model!!!!!!!!!!!!!!!! thus they apply sliding windows. 

    在训练时,the anomalies and missing points in a testing window x can bring bias to the mapped z, and further make the reconstruction probability inaccurate. 

    如何避免anomalies and missing points对训练造成的bias:

    1. missing points. adopt the MCMC-based missing data imputation technique with the trained VAE. 即模拟出missing points的可能值,然后用可能值,代替missing points 的值。
    2. anomalies

    All the algorithms evaluated in this paper compute one anomaly score for each point. A threshold can be chosen to do the decision: if the score for a point is greater than the threshold, an alert should be triggered

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  • 原文地址:https://www.cnblogs.com/dulun/p/12342402.html
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