Time series anomaly detection is one of the most important research directions in machine learning, which aims to find the patterns that deviate significantly from the normal behavior of time series. However, most of the existing methods for anomaly detection of time series are based on single-modality feature learning, which ignores the relevance and complementarity of the characteristic distribution of time series in multi-modality space, and consequently fails to make full use of the existing information for learning. To alleviate the above problems, in this paper, we present a time series anomaly detection model based on multimodal adversarial learning. Firstly, we convert the original time series into the frequency domain to construct multi-modality time series representation. Then, based on the constructed multi-modality representation, we propose a multimodal generated adversarial network model to learn normal data’s distributions in time domain and frequency domain jointly. Finally, by modeling the anomaly detection problem as the data reconstruction problem in time domain and frequency domain, we measure the anomaly score of time series from both the time domain and frequency domain perspectives. We verify the proposed method on the time series data sets of UCR and MIT-BIH. Experimental results on the 6 data sets of UCR and MIT-BIH show that, compared with the state-of-the-arts, the proposed method improves the AUC and AP metrics of anomaly detection performance by 12.50% and 21.59% respectively.