Recent developments in time-dependent services and the Internet of Things (IoT) have resulted in the broad availability of massive time series data. Subsequently, analyzing time series data became critically important due to its ability to promote diverse real-world applications such as intelligent manufacturing, smart city, business intelligence, public safety, medicine and health care, environmental management, security and monitoring, and so on. Considering the variety, volume, and dimension of time series data, traditional modelbased and statistical approaches are inadequate in many applications. Deep learning techniques have recently gone through massive growth. Deep learning models, such as Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), and Graph Neural Network (GNN), have been extensively applied in many domains such as perception, computer vision, natural language processing, and machine translation. They have drastically outperformed traditional approaches for various machine learning tasks due to their powerful learning ability. This success further inspired many recent works to adopt these deep learning models for various time series data analysis tasks, such as equipment fault detection, traffic flow prediction, financial forecasting, remote sensing data classification, fault diagnosis, natural calamity prediction, and various timebased social network services. This topical collection solicits high-quality research papers in theory, techniques, approaches, and applications using deep learning for diverse time series data processing and analysis tasks. Both researchers and practitioners are invited to present their latest research findings and engineering experiences in time series analysis and applications with deep learning techniques. 

Topics of Interest include (but is not limted to)
	Time series compression, augmentation, and dimensionality reduction with deep learning
	Heterogeneous time series fusion and analysis with deep learning
	Anomaly detection in time series with deep learning
	Deep learning for time series forecasting
	Time series clustering and classification with deep learning
	Time series motifs discovery and temporal pattern mining with deep learning
	Big time series management with deep learning
	Deep learning for time series interaction and visualization 
	Interpretable deep learning models for time series analysis
	Deep learning for analyzing chaotic or uncertain time series
	Deep learning models preserving time series data privacy and security
	Deep time series representation learning
	Deep learning for knowledge extraction, representation, and reasoning from time series data
	Deep learning with semantics for time series data
	Deep learning models for novel applications of time series data analysis

	Paper Submission:    June 30, 2021
	First decision:           September 15, 2021
	Revision due:            November 15, 2021 
	Final decision:          January 31, 2022
	Final version due:     February 28, 2022