Multivariate Time Series Anomaly Detection via Dynamic Graph Forecasting
- URL: http://arxiv.org/abs/2302.02051v1
- Date: Sat, 4 Feb 2023 01:27:01 GMT
- Title: Multivariate Time Series Anomaly Detection via Dynamic Graph Forecasting
- Authors: Katrina Chen, Mingbin Feng, Tony S. Wirjanto
- Abstract summary: We propose DyGraphAD, a time series anomaly detection framework based upon a list of dynamic inter-series graphs.
The core idea is to detect anomalies based on the deviation of inter-series relationships and intra-series temporal patterns from normal to anomalous states.
Our numerical experiments on real-world datasets demonstrate that DyGraphAD has superior performance than baseline anomaly detection approaches.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Anomalies in univariate time series often refer to abnormal values and
deviations from the temporal patterns from majority of historical observations.
In multivariate time series, anomalies also refer to abnormal changes in the
inter-series relationship, such as correlation, over time. Existing studies
have been able to model such inter-series relationships through graph neural
networks. However, most works settle on learning a static graph globally or
within a context window to assist a time series forecasting task or a
reconstruction task, whose objective is not tailored to explicitly detect the
abnormal relationship. Some other works detect anomalies based on
reconstructing or forecasting a list of inter-series graphs, which
inadvertently weakens their power to capture temporal patterns within the data
due to the discrete nature of graphs. In this study, we propose DyGraphAD, a
multivariate time series anomaly detection framework based upon a list of
dynamic inter-series graphs. The core idea is to detect anomalies based on the
deviation of inter-series relationships and intra-series temporal patterns from
normal to anomalous states, by leveraging the evolving nature of the graphs in
order to assist a graph forecasting task and a time series forecasting task
simultaneously. Our numerical experiments on real-world datasets demonstrate
that DyGraphAD has superior performance than baseline anomaly detection
approaches.
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