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

Abstract

The embodiment of the invention provides a time series data anomaly detection method and device, electronic equipment and a storage medium, and aims to improve anomaly detection efficiency and/or detection accuracy. The anomaly detection method for the time series data comprises the steps of according to a preset period, segmenting the time series data to be detected, and obtaining time series data segments with the time length being the preset period length; collecting a time series data segment, performing encoding operation on the time series data segment to obtain an encoding result, and performing decoding operation on the encoding result to obtain a decoded data segment, the encoding operation and the decoding operation being used for generating decoded data consistent with the timeseries data based on periodic time series data; and comparing the time series data segment with the decoded data segment to determine whether the time series data segment is abnormal or not.

Description

technical field [0001] The present invention relates to the field of information technology, in particular to an abnormal detection method, device, electronic equipment and storage medium of time series data. Background technique [0002] In various business scenarios, for various reasons, such as to ensure the stable operation of the business, or to optimize the business, it is usually necessary to perform anomaly detection on real-time observed indicators or log-recorded indicators during business operation. Among them, the indicators in the business are usually time-series data, such as the number of service users, traffic data, transaction volume, and so on. Moreover, normal index data is usually periodic time series data, while abnormal index data is usually aperiodic time series data, and there are usually multiple abnormal values ​​in abnormal index data, or there are abnormal data change trends . [0003] In related technologies, in order to realize anomaly detecti...

AI Technical Summary

Problems solved by technology

[0004] It can be seen that when using related technologies for anomaly detection of time series data, it is necessary to manually set multiple detection baselines from multiple detection angles, resulting in the need for a lot of manpower and time.

In addition, the artificially set detection baseline is inaccurate, which may easily lead to missed or false detection of abnormal conditions.

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