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Multi-time-series data abnormity detection method and device

An anomaly detection and sequence data technology, applied in the Internet field, can solve problems such as abnormal monitoring indicators, impact on machine performance, and performance overload

Inactive Publication Date: 2021-02-26
TSINGHUA UNIV
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  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

[0003] In actual use, due to failures such as machine aging, performance overload, and malicious attacks, the performance of the machine will inevitably be affected, and these monitoring indicators will also be abnormal.

Method used

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  • Multi-time-series data abnormity detection method and device
  • Multi-time-series data abnormity detection method and device

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Embodiment Construction

[0059] Embodiments of the present application are described in detail below, examples of which are shown in the drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below by referring to the figures are exemplary, and are intended to explain the present application, and should not be construed as limiting the present application.

[0060] The method and device for detecting anomalies in multi-time series data according to the embodiments of the present application will be described below with reference to the accompanying drawings.

[0061] Specifically, Table 1 lists the existing unsupervised algorithms and their shortcomings. The LSTM-NDT algorithm uses a deterministic method to model multiple time series. DAGMM uses a stochastic method but ignores the time dependence of time series data. LSTM- VAE is simply a combination of long short-term memory ne...

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Abstract

The invention provides a multi-time-series data abnormity detection method and device, and the method comprises the following steps: obtaining to-be-processed data, and segmenting the to-be-processeddata into multi-time-series segment data; calculating a reconstruction value of each piece of time series fragment data of the multiple pieces of time series fragment data through an offline trainingmodel; calculating the reconstruction probability of each piece of time series fragment data based on the reconstruction value of each piece of time series fragment data; and comparing the reconstruction probability of the time series fragment data corresponding to the abnormal moment with an abnormal threshold to obtain an abnormal result, and analyzing the abnormal result. Therefore, on the premise of considering the randomness and time dependence of the multi-time series, the historical normal mode of the multi-time series is learned, so that the multi-time series data exception detection of the multi-time series data is more accurate, and the output result of the model has interpretability.

Description

technical field [0001] The present application relates to the field of Internet technologies, and in particular to a multi-time series data anomaly detection method and device. Background technique [0002] Internet services are an important part of the Internet today, and these services are deployed on a large number of machines (servers, virtual machines, containers) of Internet companies. In order to ensure the reliability of these services and provide better service support for upper-layer software and systems, operation and maintenance personnel need to perform quality inspections on the operating status of machines. The machine is the hardware foundation of the entire Internet. In the operation and maintenance management work, the operation and maintenance engineer usually monitors and collects various performance indicators of the machine. These different indicators from the same machine form multiple time series. For example, a machine has indicators such as CPU usa...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F11/16
CPCG06F11/1695
Inventor 裴丹苏亚
Owner TSINGHUA UNIV
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