A spatiotemporal perception framework for multivariate time series anomaly detection

By combining variable community analysis and spatiotemporal stationarity module, the spatial correlation and temporal dependence of multivariate time series are explicitly modeled, which solves the problem of ignoring the relationship between variables in existing methods, achieves more accurate and stable anomaly detection, and improves the robustness and interpretability of the model.

CN122333045APending Publication Date: 2026-07-03SOUTHWEST PETROLEUM UNIV
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
SOUTHWEST PETROLEUM UNIV
Filing Date
2026-05-22
Publication Date
2026-07-03

AI Technical Summary

Technical Problem

Existing multivariate time series anomaly detection methods ignore the spatial correlation between variables, which leads to interference from weakly correlated variables in the reconstruction process, unstable reconstruction error signals, and insufficient generalization ability and robustness of the models in high-dimensional complex data scenarios.

Method used

The algorithm employs a variable community analysis-driven grouping and reconstruction module and a spatiotemporal stationarity-driven error enhancement module to explicitly model the spatial correlation and temporal dependence characteristics between variables. It groups variables and reconstructs them independently through a community detection algorithm, and combines spatial importance weighting and temporal stationarity smoothing to process the error signal.

Benefits of technology

It significantly improves the accuracy and robustness of anomaly detection, reduces noise interference, and enhances the interpretability and generalization ability of the model, making it suitable for real-world scenarios where anomaly samples are scarce or expensive to obtain.

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Abstract

This invention discloses a spatiotemporal awareness framework for anomaly detection in multivariate time series, belonging to the field of anomaly detection technology. It includes: a variable community analysis-driven grouping and reconstruction module for modeling the spatial relationships of variables in multivariate time series and grouping variables, and reconstructing each variable group separately; and a spatiotemporal stationarity-driven error enhancement module for spatial importance weighting and temporal stationarity smoothing of reconstruction errors, and generating an enhanced anomaly score. This invention explicitly utilizes the spatial correlation between variables through variable community analysis-driven grouping and reconstruction, grouping strongly correlated variables and reconstructing them independently, effectively reducing noise and interference introduced by weakly correlated variables, and improving reconstruction quality and model generalization ability. Through the collaborative work of two modules capturing spatiotemporal relationship features, the accuracy and robustness of anomaly detection are significantly improved.
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