Multi-mode time sequence anomaly detection method, storage medium and equipment

Abstract

The invention relates to a multi-mode time series anomaly detection method, which comprises the following steps of: constructing a multi-mode sub-time series clustering model based on time series segmentation and sub-time series clustering modes according to a historical time series; iteratively optimizing the multi-mode sub-time-sequence clustering model based on a method of initializing, splitting, combining and removing sub-time-sequence clustering modes, and solving to obtain each sub-time-sequence clustering mode; and based on the sub-time sequence clustering mode, extracting each sub-time sequence from the to-be-detected time sequence to perform anomaly detection. According to the method, various sub-time sequence modes with different lengths can be adaptively extracted from the timesequence, the situation of insufficient mode expression or interference of mode expression caused by extracting the sub-time sequence with a fixed length at present is avoided, and the time sequenceabnormity detection precision based on the situation is higher. The invention further relates to a storage medium and equipment.

Description

technical field [0001] The invention relates to the technical field of data analysis, in particular to a multi-mode time series anomaly detection method, storage medium and equipment. Background technique [0002] Time series anomaly detection has a wide range of applications, such as abnormal detection of time series data collected by sensors in industry to evaluate the performance of equipment, abnormal detection of heartbeat time series in the medical field to interpret patient conditions, and stock market time series in the economic field Perform anomaly detection to determine stock market conditions. [0003] A type of time series anomaly detection method adopts a forecast-based method, which determines whether it is abnormal through the difference between the predicted value and the real value. The forecast model usually uses a differential autoregressive moving average model (ARIMA model), Kalman filter, online support vector machines etc. For this type of method, i...

AI Technical Summary

Problems solved by technology

Some methods first extract fixed-length sub-time series, and then use traditional clustering methods for clustering, such as k-means, k-medoids, one-class SVM methods, etc., based on clustering for anomaly detection, but the extracted sub-time series Does not reflect the inherent patterns in the original time series

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