MIM-GAN-based Anomaly Detection for Multivariate Time Series Data

TL;DR

This paper introduces MIM-GAN, an unsupervised anomaly detection method for multivariate time series that enhances GAN training stability and detection accuracy using message importance measures and LSTM-based architecture.

Contribution

It proposes a novel MIM-GAN model with an improved loss function and a discriminant reconstruction score for more effective anomaly detection in multivariate time series.

Findings

Outperforms existing methods in precision, recall, and F1 score.

Avoids model collapse and local optima in GAN training.

Effectively captures temporal correlations in time series data.

Abstract

The loss function of Generative adversarial network(GAN) is an important factor that affects the quality and diversity of the generated samples for anomaly detection. In this paper, we propose an unsupervised multiple time series anomaly detection algorithm based on the GAN with message importance measure(MIM-GAN). In particular, the time series data is divided into subsequences using a sliding window. Then a generator and a discriminator designed based on the Long Short-Term Memory (LSTM) are employed to capture the temporal correlations of the time series data. To avoid the local optimal solution of loss function and the model collapse, we introduce an exponential information measure into the loss function of GAN. Additionally, a discriminant reconstruction score consisting on discrimination and reconstruction loss is taken into account. The global optimal solution for the loss function is derived and the model collapse is proved to be avoided in our proposed MIM-GAN-based anomaly detection algorithm. Experimental results show that the proposed MIM-GAN-based anomaly detection algorithm has superior performance in terms of precision, recall, and F1 score.