In order to deal with the problem of low accuracy of anomaly detection caused by data imbalance and highly complex temporal correlation of time series， a re-encoding based unsupervised time series anomaly detection model based on Generative Adversarial Network （GAN）， named RTGAN （Re-encoding Time series based on GAN）， was proposed. Firstly， multiple generators with cycle consistency were used to ensure the diversity of generated samples and thereby learning different anomaly patterns. Secondly， the stacked Long Short-Term Memory-dropout Recurrent Neural Network （LSTM-dropout RNN） was used to capture temporal correlation. Thirdly， the differences between the generated samples and the real samples were compared in the latent space by improved re-encoding. As the re-encoding errors， these differences were served as a part of anomaly score to improve the accuracy of anomaly detection. Finally， the new anomaly score was used to detect anomalies on univariate and multivariate time series datasets. The proposed model was compared with seven baseline anomaly detection models on univariate and multivariate time series. Experimental results show that the proposed model obtains the highest average F1-score （0.815） on all datasets. And the overall performance of the proposed model is 36.29% and 8.52% respectively higher than those of the original AutoEncoder （AE） model Dense-AE （Dense-AutoEncoder） and latest benchmark model USAD （UnSupervised Anomaly Detection on multivariate time series）. The robustness of the model was detected by different Signal-to-Noise Ratio （SNR）. The results show that the proposed model consistently outperforms LSTM-VAE （Variational Autoencoder based on LSTM）， USAD and OmniAnomaly， especially in the case of 30% SNR， the F1-score of RTGAN is 13.53% and 10.97% respectively higher than those of USAD and OmniAnomaly. It can be seen that RTGAN can effectively improve the accuracy and robustness of anomaly detection.