Multivariate Time Series Anomaly Detection Algorithm in Missing Value Scenario

Abstract

Time series anomaly detection is an important research field in industry.Current methods of time series anomaly detection focus on anomaly detection for complete time series data,without considering the time series anomaly detection task containing missing values caused by network anomaly and sensor damage in industrial scenarios.In this paper,we propose an attention representation-based time series anomaly detection algorithm MMAD (missing multivariate time series anomaly detection) for the more common time series anomaly detection tasks with missing values in industrial scenarios.Specifically,MMAD first models the spatial correlation of different time stamps in time series by time position coding.Then,we build an attention representation module to learn the relationships between different time stamps and represent them as an embedded high-dimensional coding matrix,thereby representing the multivariate time series with missing values as a high-dimensional representation without missing values.Finally,we design the conditional normalized flow to reconstruct the representation and use the reconstruction probability as the anomaly score,the lower the probability of reconstruction,the more abnormal the sample.Experiments on three classical time series datasets show that,the average performance of MMAD is improved by 11% comparing with other baseline methods,which verifies the efficacy of MMAD to achieve multivariate time series anomaly detection with missing values.

Keywords

• multivariate time series|anomaly detection|missing-value scenario|attention mechanism|neural network