Method and system for detecting anomalies in time series data
    1.
    发明授权
    Method and system for detecting anomalies in time series data 有权
    检测时间序列数据异常的方法和系统

    公开(公告)号:US08682816B2

    公开(公告)日:2014-03-25

    申请号:US14023061

    申请日:2013-09-10

    Applicant: Google Inc.

    CPC classification number: H04L43/04 G06F17/3089 G06Q10/06 G06Q10/063

    Abstract: A server system stores time series data for a data source. The time series data comprises a plurality of time-value pairs, each pair including a value associated with an attribute of the data source and a time. For a particular attribute, the server system generates a plurality of forecasting models for characterizing the time-value pairs, each model including an estimated attribute value and an associated error-variance. For a time-value pair, the server system determines a plurality of differences between the value of the time-value pair and respective estimated attribute values of the plurality of forecasting models and tags the time-value pair as an anomaly if the differences for at least a first subset of the forecasting models are greater than the corresponding error variances. In response to a request from a client application, the server system returns at least a subset of the time-value pairs tagged as anomalies.

    Abstract translation: 服务器系统存储数据源的时间序列数据。 时间序列数据包括多个时间值对,每对包括与数据源的属性相关联的值和时间。 对于特定属性,服务器系统生成用于表征时间值对的多个预测模型,每个模型包括估计的属性值和相关的误差方差。 对于时间值对,服务器系统确定时间值对的值与多个预测模型的各个估计属性值之间的多个差异,并且将时间 - 值对作为异常标记,如果在 预测模型的最小子集大于对应的误差方差。 响应于来自客户端应用的请求,服务器系统返回标记为异常的时间 - 值对的至少一个子集。

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