RGAnomaly: Data reconstruction-based generative adversarial networks for multivariate time series anomaly detection in the Internet of Things

Abstract

The Internet of Things encompasses a variety of components, including sensors and controllers, which generate vast amounts of multivariate time series data. Anomaly detection within this data can reveal patterns of behavior that deviate from normal operating states, providing timely alerts to mitigate potential serious issues or losses. The prevailing methodologies for multivariate time series anomaly detection are based on data reconstruction. However, these methodologies face challenges related to insufficient feature extraction and fusion, as well as instability in the reconstruction effectiveness of a single model. In this article, we propose RGAnomaly, a novel data reconstruction-based generative adversarial network model. This model leverages transformers and cross-attention mechanisms to extract and fuse the temporal and metric features of multivariate time series. RGAnomaly constructs a joint generator comprising an autoencoder and a variational autoencoder, which forms the adversarial structure with a discriminator. The anomaly score is derived from the combined data reconstruction loss and discrimination loss, providing a more comprehensive evaluation for anomaly detection. Comparative experiments and ablation studies on four public multivariate time series datasets demonstrate that RGAnomaly delivers superior performance in anomaly detection, effectively identifying anomalies in time series data within IoT environments.