Time Series Classification Based on Data-Augmented Contrastive Learning

Junyao Wang, Jiangyi Hu, Taishan Xu, Xiancheng Ren, Wenzhong Li

Abstract: Time series classification has become a popular research topic in data mining and has a wide range of applications in many fields in daily life. When analyzing and classifying time series, it is challenging to address their dynamic distribution characteristics and preserve key temporal information. In this paper, we propose a novel time series classification algorithm based on data-augmented contrastive learning. The proposed model consists of four parts, the Data Augmentation module, the Encoder, the Feature Space Contrastive Learning module and the Classifier. The four parts work together to jointly accomplish the task of time series classification. During the process of training the time series representation encoder, we adopt a loss function combining contrastive loss and classification loss to optimize the encoder, which can learn label-related representations from time series data and extract internal features. We conduct extensive experiments based on 30 open datasets, which show that the proposed method outperforms the state-of-the-art baseline algorithms.