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

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Link: https://www.springer.com/journal/521/updates/18687164
 
When N/A
Where N/A
Submission Deadline Jun 30, 2021
Notification Due Sep 15, 2021
Final Version Due Feb 28, 2022
Categories    deep learning   time series
 

Call For Papers

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.

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