The complex time correlation and high dimension of multivariable time series lead to poor anomaly detection performance. In view of this, an unsupervised anomaly detection model of multivariable time series based on graph autoencoders (GAEs) is proposed with the adversarial training framework as the basis. First, features are converted into embedded vectors. Secondly, the divided time series and embedded vectors are converted into graph-structured data. Then, two GAEs are used to simulate the adversarial training and reconstruct data samples. Finally, the anomaly is determined according to the reconstruction error of the test data under the model training. The proposed method is compared with five baseline anomaly detection methods. The experimental results show that the proposed model achieves the highest F1 score on the test data set, and the overall performance F1 score is 28.4% higher than that of the latest anomaly detection model, namely, USAD. Therefore, it can be seen that the proposed model can effectively improve the performance of anomaly detection.