One-dimensional time sequence anomaly detection method and device and computer equipment

Abstract

The invention relates to a one-dimensional time sequence anomaly detection method and device and computer equipment. The method comprises the following steps: extracting a to-be-predicted point from a one-dimensional time sequence, and extracting to-be-predicted context information of the to-be-predicted point through a sliding window; carrying out dimensionality reduction on the context information to be predicted by adopting an encoder to obtain low-dimensional embedded data to be predicted; querying a plurality of neighbor data of the to-be-predicted low-dimensional embedded data in the detection set; according to the sample performance vector of the neighbor data, obtaining the probability that the base detector sequence correctly detects the one-dimensional time sequence; obtaining a base detector with the highest detection performance according to the probability of correctly detecting the one-dimensional time sequence by the base detector sequence; and performing anomaly detection on the one-dimensional time sequence according to the base detector with the highest detection performance. By adopting the method, the time sequence anomaly detection performance can be improved.

Description

technical field [0001] The present application relates to the technical field of data processing, in particular to a one-dimensional time series anomaly detection method, device and computer equipment. Background technique [0002] At present, large Internet companies need to closely monitor the real-time performance of their systems, and a short service interruption or quality degradation may cause huge business losses. These real-time performance data (eg, search response time, CPU usage) are usually collected and stored in time series. To ensure the smooth running of business operations, these companies usually develop anomaly detection systems that can accurately detect time-series anomalies and troubleshoot them in a timely manner. [0003] However, the amount of time series data in large companies is extremely large, and the characteristics of these data are very different, and the existing time series anomaly detection algorithms have poor versatility. Each anomaly d...

AI Technical Summary

Problems solved by technology

[0003] However, the amount of time series data in large companies is extremely large, and the characteristics of these data are very different, and the existing time series anomaly detection algorithms have poor versatility. Each anomaly detection algorithm has its applicable data type, so a single model is used for The accuracy of time series data anomaly detection is not high

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