Memory-augmented Adversarial Autoencoders for Multivariate Time-series Anomaly Detection with Deep Reconstruction and Prediction

10/15/2021
by   Qinfeng Xiao, et al.
1

Detecting anomalies for multivariate time-series without manual supervision continues a challenging problem due to the increased scale of dimensions and complexity of today's IT monitoring systems. Recent progress of unsupervised time-series anomaly detection mainly use deep autoencoders to solve this problem, i.e. training on normal samples and producing significant reconstruction error on abnormal inputs. However, in practice, autoencoders can reconstruct anomalies so well, due to powerful capabilites of neural networks. Besides, these approaches can be ineffective for identifying non-point anomalies, e.g. contextual anomalies and collective anomalies, since they solely utilze a point-wise reconstruction objective. To tackle the above issues, we propose MemAAE (Memory-augmented Adversarial Autoencoders with Deep Reconstruction and Prediction), a novel unsupervised anomaly detection method for time-series. By jointly training two complementary proxy tasks, reconstruction and prediction, with a shared network architecture, we show that detecting anomalies via multiple tasks obtains superior performance rather than single-task training. Additionally, a compressive memory module is introduced to preserve normal patterns, avoiding unexpected generalization on abnormal inputs. Through extensive experiments, MemAAE achieves an overall F1 score of 0.90 on four public datasets, significantly outperforming the best baseline by 0.02.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
04/04/2019

Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection

Deep autoencoder has been extensively used for anomaly detection. Traini...
research
10/06/2021

Anomaly Transformer: Time Series Anomaly Detection with Association Discrepancy

Unsupervisedly detecting anomaly points in time series is challenging, w...
research
08/17/2023

Beyond Sharing: Conflict-Aware Multivariate Time Series Anomaly Detection

Massive key performance indicators (KPIs) are monitored as multivariate ...
research
01/30/2023

BSSAD: Towards A Novel Bayesian State-Space Approach for Anomaly Detection in Multivariate Time Series

Detecting anomalies in multivariate time series(MTS) data plays an impor...
research
05/07/2023

Efficient pattern-based anomaly detection in a network of multivariate devices

Many organisations manage service quality and monitor a large set device...
research
07/07/2023

Dynamic Graph Attention for Anomaly Detection in Heterogeneous Sensor Networks

In the era of digital transformation, systems monitored by the Industria...
research
11/16/2022

Are we certain it's anomalous?

The progress in modelling time series and, more generally, sequences of ...

Please sign up or login with your details

Forgot password? Click here to reset