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

Abstract

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.