Abstract:

Currently,most time series lack corresponding anomaly labels and existing reconstruction-based anomaly detection algorithms fail to capture the complex underlying correlations and temporal dependencies among multidimensional data effectively.To construct feature-rich time series,a multi-scale feature information fusion anomaly detection model is proposed.First,the model employs convolutional neural networks to perform feature convolution on different sequences within sliding windows,capturing local contextual information at different scales.Then,position encoding from the Transformer is utilized to embed the convolved time series windows,enhancing the positional relationships between each time series and its neighboring sequences within the sliding window.Time attention is introduced to capture the temporal autocorrelation of the data,and multi-head self-attention adaptively assigns different weights to different time series within the window.Finally,the reconstructed window data obtained through the down-sampling process is progressively fused with the local features and temporal context information at different scales.This process accurately reconstructs the original time series,with the reconstruction error used as the final anomaly score for anomaly determination.Experimental results indicate that the constructed model achieves improved F1 scores compared to the baseline models on both the SWaT and SMD datasets.On the high-dimensional and imbalanced WADI dataset,the F1 score decreases by 1.66% compared to the GDN model.

Key words: anomaly detection, multi-scale information fusion, convolutional neural network, transformer, multidimensional time series, autoencoder