Deep learning models for time series anomaly detection are mainly divided into supervised learning, semi-supervised learning, and unsupervised learning. Unsupervised learning has grown in importance for identifying anomaly features in time series without labels. Unsupervised learning of anomaly detection needs to be improved in two aspects. One is the calculation time, the other is the anomaly score. Some excellent unsupervised learning algorithms utilize clustering algorithms to filter out normal features, which takes too much time. Moreover, these algorithms use a fixed-length window to calculate anomaly scores, leading to inaccuracies and fluctuations. This paper proposes a Temporal Autoencoder Dynamic Series (TADS) framework. Here are the original contributions of the paper. Firstly, in the process of network compression coding, an enhanced Channel Attention Mechanism is used to identify critical features. Secondly, it uses Dynamic Series to replace the fixed window which improves the inaccuracy and fluctuation and also avoids the random error behavior caused by parameter adjustment. In addition, it is unnecessary to use clustering algorithms to obtain normal features. Finally, on the public MIT-BIH dataset, the experimental results show that TADS can improve the F1 score from 79% to 91% and accuracy from 89% to 93%. Meanwhile, the calculation time and fluctuation decreased by 42% and 19.8%, respectively. The experimental results demonstrate the performance of TADS for unsupervised anomaly detection on the MIT-BIH time series of ECG dataset through qualitative and quantitative methods.