A Label-Free Time Series Anomaly Detection Method

A technology of category labeling and time series, applied in the field of time series anomaly detection, can solve the problems of manually setting the number of clusters in hierarchical clustering and unsatisfactory segmentation effect of fixed points of satellite telemetry data, so as to achieve compact and coupled segmentation results. high degree of effect
CN104899327BActive Publication Date: 2018-03-30HARBIN INST OF TECH
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
HARBIN INST OF TECH
Publication Date
2018-03-30

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Abstract

A time series anomaly detection method without category label relates to a time series anomaly detection method without category label. The purpose of the present invention is to solve the problem that the fixed point segmentation effect for satellite telemetry data is not ideal, the number of clusters needs to be manually set for hierarchical clustering, and there is currently no directly available offline and label-free time series that can be used. Problems in the framework of online anomaly detection methods. It is achieved by the following technical solutions: step 1, segment the historical satellite telemetry data according to the periodic characteristics of the satellite telemetry data, and obtain a time series X={x1,x2,...,xn} without category labels; step 2, pair the step Once the obtained X={x1,x2,...,xn}, perform adaptive hierarchical clustering, and determine and delete abnormal sequences in the time series without class labels, and obtain the sum; step 3, combine the matching threshold with the sum as the sample, The nearest neighbor algorithm is used to perform pattern matching on x" to realize abnormal detection of satellite telemetry data. The invention is applied to the field of satellite data detection.
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Description

technical field

[0001] The invention relates to a time series anomaly detection method without category labels. Background technique

[0002] By analyzing the yaw attitude angle in the satellite telemetry data, the overall change trend of the yaw attitude angle is as follows: figure 2 As shown, its details change as image 3 As shown, the satellite telemetry data has obvious periodicity, and this characteristic has been confirmed with the satellite telemetry data provider. By analyzing each period of the telemetry data, it can be concluded whether the satellite’s operating status within the period is normal, and the effect of segmenting the satellite telemetry data according to the fixed point is not ideal, such as Figure 4 As shown, the coupling degree between each sub-sequence is not high enough, there is a certain deviation, and this deviation will become more and more obvious as time goes on.

[0003] At present, there are no clear reference materials for the normal ...

Claims

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