Using Multi-Scale Convolution Fusion and Memory-Augmented Adversarial Autoencoder to Detect Diverse Anomalies in Multivariate Time Series  

作　　者：Zefei Ning Hao Miao Zhuolun Jiang Li Wang 

机构地区：[1]College of Computer Science and Technology(College of Data Science),Taiyuan University of Technology,Jinzhong 030600,China

出　　处：《Tsinghua Science and Technology》2025年第1期234-246,共13页清华大学学报自然科学版(英文版)

基　　金：supported by the National Key Research and Development Program of China(No.2021YFB3300503);the Regional Innovation and Development Joint Fund of NSFC(No.U22A20167).

摘　　要：Time series anomaly detection is an important task in many applications,and deep learning based time series anomaly detection has made great progress.However,due to complex device interactions,time series exhibit diverse abnormal signal shapes,subtle anomalies,and imbalanced abnormal instances,which make anomaly detection in time series still a challenge.Fusion and analysis of multivariate time series can help uncover their intrinsic spatio-temporal characteristics,and contribute to the discovery of complex and subtle anomalies.In this paper,we propose a novel approach named Multi-scale Convolution Fusion and Memory-augmented Adversarial AutoEncoder(MCFMAAE)for multivariate time series anomaly detection.It is an encoder-decoder-based framework with four main components.Multi-scale convolution fusion module fuses multi-sensor signals and captures various scales of temporal information.Self-attention-based encoder adopts the multi-head attention mechanism for sequence modeling to capture global context information.Memory module is introduced to explore the internal structure of normal samples,capturing it into the latent space,and thus remembering the typical pattern.Finally,the decoder is used to reconstruct the signals,and then a process is coming to calculate the anomaly score.Moreover,an additional discriminator is added to the model,which enhances the representation ability of autoencoder and avoids overfitting.Experiments on public datasets demonstrate that MCFMAAE improves the performance compared to other state-of-the-art methods,which provides an effective solution for multivariate time series anomaly detection.

关 键 词：multivariate time series anomaly detection AutoEncoder(AE) multi-scale fusion 

分 类 号：G63[文化科学—教育学]