Markov process-based time series stream data anomaly detection method

Abstract

The invention discloses a Markov process-based time series stream data anomaly detection method. The method comprises the following steps of s1, selecting a training data stream; s2, carrying out dimensionality reduction on the training data flow through an LPP algorithm; s3, determining a current mode category through a K-Means clustering algorithm based on an elbow strategy, and performing mode division on the training data stream; s4, constructing a Markov anomaly detection model, and s5, processing to-be-tested data, inputting the processed to-be-tested data into the Markov anomaly detection model, and outputting an anomaly detection result. According to the method, the time series stream data is clustered, and the data is divided into different modes to construct the Markov-based anomaly detection model; in the model, normal conversion between the different modes is identified as concept drift, only mode conversion which cannot occur is identified as anomaly, the distribution rule of the model and the time series flow data is more stable, and the detection accuracy is higher.

Description

technical field [0001] The invention relates to the technical field of statistics, in particular to a Markov process-based time series flow data anomaly detection method. Background technique [0002] With the development of the Internet age and changes in data generation, data is constantly changing. In various fields, it often appears in the form of time series flow data, such as earthquake monitoring, atmospheric environment monitoring, network monitoring and other fields. Time series streaming data is generated quickly, with a large amount of data and high dimensionality. Anomaly detection in time series streaming data is an important problem that urgently needs to be solved. However, traditional anomaly detection methods only focus on static data, leading to potential limitations. Therefore, research on anomaly detection methods for time series streaming data is of great significance. Furthermore, distributional changes in time-series flow data indicate that there m...

AI Technical Summary

Problems solved by technology

The above detection methods ignore the problem of concept drift and are not suitable for anomaly detection of streaming data with concept drift

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