Abstract
Currently, multivariate time series anomaly detection has made great progress in many fields and occupied an important position. The common limitation of many related studies is that there is only temporal pattern without capturing the relationship between variables and the loss of information leads to false warnings. Our article proposes an unsupervised multivariate time series anomaly detection. In the prediction part, multiscale convolution and graph attention network are mainly used to capture information in temporal pattern with feature pattern. The threshold selection part uses the root mean square error between the predicted value and the actual value to perform extreme value analysis to obtain the threshold. Finally, the model in this paper outperforms other latest models on actual datasets.
Highlights
Detection of time series data has always been a hot issue in academia and industry. e detection of abnormal points and the location of abnormal areas can provide important information at critical moments, so that people can intervene with abnormal events in a targeted way to prevent or eliminate abnormal events
Ding et al [6] proposed RADM, a real-time anomaly detection algorithm based on Hierarchical Temporal Memory (HTM) and Bayesian Network (BN), which improved the performance of real-time anomaly detection
When analysing real-world datasets, a common requirement is to find out those instances that can be considered as outliers, which are significantly different from most other points. e goal of the anomaly detection task is to be data-driven to find abnormal of all samples
Summary
Detection of time series data has always been a hot issue in academia and industry. e detection of abnormal points and the location of abnormal areas can provide important information at critical moments, so that people can intervene with abnormal events in a targeted way to prevent or eliminate abnormal events. Malhotra et al [4] proposed an encoder-decoder network based on LSTM, which modelled the reconstruction probability of “normal” time series and used reconstruction errors to detect anomalies in multiple sensors. Ding et al [6] proposed RADM, a real-time anomaly detection algorithm based on Hierarchical Temporal Memory (HTM) and Bayesian Network (BN), which improved the performance of real-time anomaly detection. (1) To the best of our knowledge, this is the first study on multivariate time series anomaly detection generally from a graph-based perspective with graph attention network in forecast (2) We propose a new model that combines temporal with feature pattern, capturing more latent relationship between variables (3) Experimental results show that our method outperforms the state-of-the-art methods on 3 benchmarks e arrangement of this article is as follows.
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