NCAA 2022 : Topical Collection on Deep Learning for Time Series Data for Neural Computing and Applications

Submission Deadline: Jun 30, 2021
Notification of Acceptance: Sep 15, 2021
Camera Ready Version Due : Feb 28, 2022


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. 

Call For Paper

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

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