To address the issues of the existing time series anomaly detection methods， such as the heavy reliance on labeled data and the insufficient modeling capability in complex scenarios， a Mamba-ADCL （Anomaly Detection in time series via Contrastive Learning with Mamba） was proposed. In the method， a dual-branch Mamba structure was designed， in which a shared-weight framework and a contrastive learning strategy were employed to enhance the feature discrepancies between normal and abnormal samples effectively， thereby improving model robustness and generalization. Meanwhile， a representation method with collaborative time-domain and frequency-domain modeling was introduced， in which time series data was transformed into frequency representations and combined with time-domain data to construct a dual-view contrastive learning mechanism jointly， thereby enhancing the recognition ability for abnormal patterns. Experimental results on four public datasets， MSL， SMAP， SWaT， and PSM show that the F1-score of Mamba-ADCL is improved by 2.22， 0.22， 3.86， and 0.35 percentage points compared with that of AnomalyTrans （Anomaly Transformer）， and by 2.43， 0.52， 0.70， and 0.28 percentage points compared with that of TFMAE （Temporal-Frequency Masked AutoEncoder）， which verifying the effectiveness of Mamba-ADCL in time series anomaly detection.