公开(公告)号：US20160062950A1

公开(公告)日：2016-03-03

申请号：US14585675

申请日：2014-12-30

Applicant: Google Inc.

Inventor： Kay H. Brodersen ,  Havard Garnes ,  Dimitris Meretakis ,  Olaf Bachmann ,  Steven Lee Scott

IPC: G06F17/18

CPC classification number: G06F17/18 ,  G06K9/6284

Abstract: Systems and methods for anomaly detection and guided analysis using structural time-series model. A server may receive a request from a client to analyze a time-series data comprising a plurality of data points. A database of global calendars may be accessed. A structural time-series model may be built from the time-series data and the database of global calendars, the structural time-series model comprising a hidden structure and a plurality of probability distributions, each probability distribution corresponding to a data point. For each data point of the time-series data, a range of expected values is determined from a respective probability distribution, the range of expected values capturing a predefined percentage of the respective probability distribution. An anomaly is detected at a first data point of the time-series data responsive to comparing the first data point with a respective range of expected values. The anomaly is transmitted to the client for display with the time-series data.

Abstract translation: 使用结构时间序列模型进行异常检测和指导分析的系统和方法。 服务器可以从客户端接收请求以分析包括多个数据点的时间序列数据。 可以访问全局日历的数据库。 可以从时间序列数据和全局日历的数据库构建结构时间序列模型，结构时间序列模型包括隐藏结构和多个概率分布，每个概率分布对应于数据点。 对于时间序列数据的每个数据点，从相应的概率分布确定预期值的范围，所述期望值的范围捕获相应概率分布的预定百分比。 响应于将第一数据点与期望值的相应范围进行比较，在时间序列数据的第一数据点处检测到异常。 通过时间序列数据将异常传输给客户端进行显示。