Abstract: It is difficult to intuitively define the similarity between time series by deep clustering methods which rely heavily on complex feature extraction networks and clustering algorithms.Contrastive learning can define the interval similarity of time series from the perspective of positive and negative sample data and jointly optimize feature extraction and clustering.Based on the contrastive learning,this paper proposes a time series clustering model that does not rely on complex representation networks.In order to solve the problem that the existing time series data enhancement methods cannot describe the transformation invariance of time series,this paper proposes a new data enhancement method that captures the similarity of sequences while ignoring the time domain characteristics of data.The proposed clustering model constructs positive and negative sample pairs by setting diffe-rent shape transformation parameters,learns feature representation,and uses cross-entropy loss to maximize the similarity of positive sample pairs and minimize negative sample pairs at the instance-level and cluster-level comparison.The proposed model can jointly learn feature representation and cluster assignment in end-to-end fashion.Extensive experiments on 32 datasets in UCR show that the proposed model can obtain equal or better performance than existing methods without relying on a specific representation learning network.

Key words: Time series clustering, Contrastive learning, Data enhancement, Representation learning, Jointly optimization