Time-series representation learning via temporal and contextual contrasting

• المؤلفون الرئيسيون: Eldele, Emadeldeen, Mohamed Ragab, Chen, Zhenghua, Wu, Min, Kwoh, Chee Keong, Li, Xiaoli, Guan, Cuntai
• مؤلفون آخرون: School of Computer Science and Engineering
• التنسيق: Conference or Workshop Item
• اللغة: English
• منشور في: 2022
• الموضوعات: Engineering::Computer science and engineering; Deep Learning; Semi-Supervised Learning; Time-Series; Machine Learning
• الوصول للمادة أونلاين: https://hdl.handle.net/10356/155626
• المؤسسة: Nanyang Technological University

Learning decent representations from unlabeled time-series data with temporal dynamics is a very challenging task. In this paper, we propose an unsupervised Time-Series representation learning framework via Temporal and Contextual Contrasting (TS-TCC), to learn time-series representation from unlabe...