DTS-MixNet: Dynamic Spatiotemporal Graph Mixed Network for Anomaly Detection in Multivariate Time Series
Chengxun Tan, Jiayi Hu, Jianchun Li, Minmin Miao, Wenjun Hu, Shitong Wang · Big Data and Cognitive Computing · 2025
Anomaly detection in multivariate time series (MTS) remains challenging due to the presence of complex and dynamic spatiotemporal dependencies. To address this, we propose the Dynamic Spatiotemporal Graph Mixed Network (DTS-MixNet), which takes a sliding window data as input to predict the next time series data and determine its state. The model comprises five blocks. The Temporal Graph Structure Learner (TGSL) generates the attention-weighted graphs via two types of neighbor relationships and the multi-head-attention-based neighbor degrees. Then, the Cross-Temporal Dynamic Encoder (CTDE) aggregates the cross-temporal dependencies from attention-weighted graphs, and encodes them into a proxy multivariate sequence (PMS), which is fed into the proposed Cross-Variable Dynamic Encoder (CVDE). Subsequently, the CVDE captures the sensors-among spatial relationship through multiple local spatial graphs and a global spatial graph, and produces a spatial graph sequence (SGS). Finally, the Spatiotemporal Mixer (TSM) mixes PMS and SGS to build a spatiotemporal mixed sequence (TSMS) for downstream tasks, e.g., classification or prediction. We evaluate on two industrial control datasets and discuss applicability to non-industrial multivariate time series. The experimental results on benchmark datasets show that the proposed DTS-MixNet is encouraging.