text: Summary: The authors proposed an unsupervised time series clustering methods built with deep neural networks. The proposed model is equipped with an encoder-decoder and a clustering model. First, the encoder employs CNN to shorten the time series and extract local temporal features, and the CNN is followed by bidirectional LSTMs to get the encoded representations. A temporal clustering model and a DCNN decoder are applied on the encoded representations and jointly trained. An additional heatmap generator component can be further included in the clustering model. The authors compared the proposed method with hierarchical clustering with 4 different temporal similarity methods on several univariate time series datasets. Detailed comments:The problem of unsupervised time series clustering is important and challenging. The idea of utilizing deep learning models to learn encoded representations for clustering is interesting and could be a promising solution. One potential limitation of the proposed method is that it is only designed for univariate time series of the same temporal length, which limits the usage of this model in practice. In addition, given that the input has fixed length, clustering baselines for static data can be easily applied and should be compared to demonstrate the necessity of temporal clustering. Some important details are missing or lack of explanations. For example, what is the size of each layer and the dimension of the encoded space? How much does the model shorten the input time series and how is this be determined? How does the model combine the heatmap output (which is a sequence of the same length as the time series) and the clustering output (which is a vector of size K) in Figure 1? The heatmap shown in Figure 3 looks like the negation of the decoded output (i.e., lower value in time series -> higher value in heatmap). How do we interpret the generated heatmap? From the experimental results, it is difficult to judge which method/metric is the best. For example, in Figure 4, all 4 DTC-methods achieved the best performance on one or two datasets. Though several datasets are evaluated in experiments, they are relatively small. Even the largest dataset (Phalanges OutlinesCorrect) has only 2 thousand samples, and the best performance is achieved by one of the baseline, with AUC score only 0.586 for binary classification. Minor suggestion: In Figure 3, instead of showing the decoded output (reconstruction), it may be more helpful to visualize the encoded time series since the clustering method is applied directly on those encoded representations.