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Time series data anomaly detection method and device, equipment and storage medium

A technology for time series data and anomaly detection, applied in the field of data processing, can solve problems such as low precision rate, false alarms, and inability to give abnormal results, so as to improve the precision rate and solve the effect of low precision rate.

Pending Publication Date: 2020-06-05
GUANGZHOU HUYA TECH CO LTD
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Existing unsupervised anomaly detection algorithms can only perform anomaly detection on some simple and regular time-series data, and the precision rate of anomaly detection is low, there are many false alarms, and specific abnormal results cannot be given. It cannot meet the user's anomaly detection requirements for time series data

Method used

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  • Time series data anomaly detection method and device, equipment and storage medium
  • Time series data anomaly detection method and device, equipment and storage medium
  • Time series data anomaly detection method and device, equipment and storage medium

Examples

Experimental program
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Effect test

Embodiment 1

[0055] figure 1It is a flow chart of a time series data anomaly detection method in Embodiment 1 of the present invention. This embodiment is applicable to the situation of real-time unsupervised anomaly detection of time series data, and the method can be executed by a time series data anomaly detection device. The device can be realized by hardware and / or software, and can generally be integrated in equipment providing abnormal point detection services. Such as figure 1 As shown, the method includes:

[0056] Step 110, according to the historical time series data in the historical time period, determine the first data security interval matching the historical time period and the second data security interval matching the future time period.

[0057] In this embodiment, the data security interval includes: time information and the value range of the security data that matches the time information, where the time information is the timestamp, and the security data is non-abn...

Embodiment 2

[0073] figure 2 It is a flow chart of a time series data anomaly detection method in Embodiment 2 of the present invention, and this embodiment can be combined with various optional solutions in the foregoing embodiments. Specifically, refer to figure 2 , the method may include the following steps:

[0074] Step 210, according to the historical time series data in the historical time period, determine a first data security interval matching the historical time period and a second data security interval matching the future time period.

[0075] Optionally, according to the historical time series data in the historical time period, determining the first data security interval matching the historical time period and the second data security interval matching the future time period may include: The time series data is input into the Prophet prediction model to obtain the first data safety interval matching the historical time period and the second data safety interval matching...

Embodiment 3

[0088] image 3 It is a schematic structural diagram of a time-series data anomaly detection device in Embodiment 3 of the present invention. This embodiment is applicable to real-time unsupervised anomaly detection of time-series data. Such as image 3 As shown, the time series data anomaly detection device includes:

[0089] The determining module 310 is configured to determine a first data security interval matching the historical time period and a second data security interval matching the future time period according to the historical time series data in the historical time period. The data security interval includes: time information and The value range of the security data matching the time information;

[0090] An adjustment module 320, configured to adjust the second data security interval according to the hit situation of each historical time series data on the first data security interval;

[0091] The detection module 330 is configured to perform anomaly detecti...

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Abstract

The embodiment of the invention discloses a time series data anomaly detection method and device, equipment and a storage medium. The method comprises the steps: according to historical time series data in a historical time period, determining a first data security interval matched with the historical time period and a second data security interval matched with a future time period, wherein the data security intervals comprise time information and the value range of security data matched with the time information; adjusting the second data security interval according to the hit condition of each historical time series data to the first data security interval; and performing anomaly detection on the real-time time series data acquired in the future time period according to the adjusted second data security interval. According to the technical scheme of the embodiment of the invention, the precision ratio of real-time anomaly detection on the time series data is improved.

Description

technical field [0001] Embodiments of the present invention relate to the technical field of data processing, and in particular, to a time series data anomaly detection method, device, device, and storage medium. Background technique [0002] With the development of the Internet and artificial intelligence, various industries use algorithms to monitor the abnormal points of some time-series data, such as time-series data such as the number of visits to some websites and the success rate of services. By monitoring these key time-series data indicators, it can be manually processed in time to reduce losses when abnormalities occur. [0003] At present, artificial intelligence algorithms use two types of methods for anomaly detection of time series data, unsupervised anomaly detection algorithms and supervised anomaly detection algorithms. Existing unsupervised anomaly detection algorithms can only perform anomaly detection on some simple and regular time-series data, and the ...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F16/2458
CPCG06F16/2474
Inventor 郑健彦高晓宇毛茂德潘建宁唐欣语
Owner GUANGZHOU HUYA TECH CO LTD
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