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EXTRACTING SEASONAL, LEVEL, AND SPIKE COMPONENTS FROM A TIME SERIES OF METRICS DATA

机译:从一系列时间数据中提取季节,水平和峰值成分

摘要

Certain embodiments involve extracting seasonal, level, and spike components from a time series of metrics data, which describe interactions with an online service over a time period. For example, an analytical system decomposes the time series into latent components that include a seasonal component series, a level component series, a spike component series, and an error component series. The decomposition involves configuring an optimization algorithm with a constraint indicating that the time series is a sum of these latent components. The decomposition also involves executing the optimization algorithm to minimize an objective function subject to the constraint and identifying, from the executed optimization algorithm, the seasonal component series, the level component series, the spike component series, and the error component series that minimize the objective function. The analytical system outputs at least some latent components for anomaly-detection or data-forecasting.
机译:某些实施例涉及从度量数据的时间序列中提取季节,水平和峰值分量,其描述了在一段时间内与在线服务的交互。例如,分析系统将时间序列分解为潜在成分,其中包括季节性成分系列,水平成分系列,峰值成分系列和误差成分系列。分解涉及配置具有约束条件的优化算法,该约束条件指示时间序列是这些潜在分量的总和。分解还包括执行优化算法以使受约束的目标函数最小化,并从执行的优化算法中识别季节性成分序列,水平成分序列,尖峰成分序列和使目标最小化的误差成分序列。功能。分析系统至少输出一些潜在成分,用于异常检测或数据预测。

著录项

  • 公开/公告号US2020218721A1

    专利类型

  • 公开/公告日2020-07-09

    原文格式PDF

  • 申请/专利权人 ADOBE INC.;

    申请/专利号US202016821132

  • 申请日2020-03-17

  • 分类号G06F16/2458;G06F16/248;

  • 国家 US

  • 入库时间 2022-08-21 11:20:08

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