UMTS-Mixer:Time Series Anomaly Detection Based on Temporal Correlation and Channel Correlation
Anomaly detection in multivariate time series is a challenging problem that requires models to learn information representations from complex temporal dynamics and derive a distinguishable criterion that can identify a small number of outliers from a large number of normal time points.However,in time series analysis,the complex temporal correlation and high dimensionality of multivariate time series will result in poor anomaly detection performance.To this end,this study proposes a model based on MLP(multi-layer perceptron)architecture(UMTS-Mixer).Since the linear structure of MLP is sensitive to order,it is employed to capture temporal correlation and cross-channel correlation.A large number of experiments show that UMTS-Mixer can detect time series anomalies and perform better on the four benchmark datasets.Meanwhile,the highest F1 is 91.35％and 92.93％on the MSL and PSM datasets,respectively.
NETL
NSTL
万方数据
维普
