Method and device for detecting abnormal point in time sequence

Abstract

The embodiment of the invention discloses a method and device for detecting an abnormal point in a time sequence. The method comprises the following steps: acquiring a discrete point in a business index time sequence; carrying out clustering prediction on a time subsequence comprising the discrete point, and determining a first prediction result of the discrete point; obtaining an error value from an index value of the discrete point through decomposition according to the time sequence decomposition method, and determining a second prediction result of the discrete point according to the error value; and determining an anomaly detection result of the discrete point according to the first prediction result and the second prediction result. The method and device adopting the technical scheme has the advantages that the abnormal point identification accuracy can be improved; and compared with the baseline detection mode in the prior art, the method and device achieves a much lower occurrence rate of abnormal point false alarms.

Description

technical field [0001] The present application relates to the technical field of data processing, in particular to a method and device for detecting abnormal points in time series. Background technique [0002] In recent years, various products based on the Internet platform have provided users with a variety of convenient services, such as shopping, financial management, insurance and so on. For various Internet products, ensuring the user's account and operation security is the prerequisite for users' peace of mind and trust. Therefore, at present, the information security departments of various Internet products can analyze the massive historical data, transform the risk control business experience into prevention and control strategies and intelligent models, and build a large and complex security protection system to ensure the security of users' accounts. However, the Internet security field is constantly attacking and defending, and lawbreakers will constantly change...

AI Technical Summary

Problems solved by technology

However, security business indicators often contain tens of thousands of indicators. With the continuous increase of policies and indicators of concern, more and more rules need to be configured, and the monitoring method becomes extremely complicated, difficult to manage and maintain

Moreover, manual configuration rules based on historical experience cannot flexibly respond to changes in the risk situation and make adaptive adjustments. Therefore, it will bring quite a lot of false alarms (high false alarm rate), and the accuracy rate is low

After the relevant personnel receive too many false alarms, they will reduce their trust and patience in the security indicator business monitoring method. When an indicator abnormality actually occurs, it is easy to ignore it and miss the best time to deal with it.

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