Sensor-Based Time-Series Analysis Empowered by Artificial Intelligence
A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Intelligent Sensors".
Deadline for manuscript submissions: 15 November 2026 | Viewed by 3
Special Issue Editors
Interests: time series; artificial intelligence; network science; new sensor technology; brain-computer fusion; multiphase flow; intelligent medicine
Interests: multi-source information fusion; new sensor technology; network science; multiphase flow detection; brain-computer fusion and hybrid intelligence; intelligent medicine; brain-controlled rehabilitation robots
Interests: complex systems and complex networks; complexity theory in artificial intelligence; evolutionary game theory
Special Issue Information
Dear Colleagues,
Rapid advances in Artificial Intelligence and Network Science are reshaping sensor-based time-series modeling, with broad applicability across domains and settings. Current research spans self-supervised and generative learning, state-space models, efficient long-sequence modeling, dynamic graphs, and topological structure learning. Meanwhile, common challenges in real-world data, such as long-range dependencies, asynchronous/irregular sampling, low SNR with structured artifacts, and covariate or concept drift, raise higher demands on generalization, interpretability, and engineering deployment.
This Special Issue centers on “sensor data-driven time-series methods and systems”, in alignment with the scope of Sensors. It welcomes application-agnostic research aimed at advancing foundational theory, algorithmic methods, and efficient systems for time-series analysis. We especially encourage work at the intersection of Sensor-based Time Series × Artificial Intelligence × Network Science and place no restrictions on application areas, welcoming signal-analysis submissions from diverse domains and settings (for instance, biomedical signals, physiological monitoring and brain-computer interface technology). Any method or practice centered on sensor or sensed data falls within the scope of this Special Issue.
Dr. Weidong Dang
Prof. Dr. Zhong-Ke Gao
Prof. Dr. Chengyi Xia
Guest Editors
Dr. Dongmei Lv
Guest Editor Assistant
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Keywords
- sensor data-driven time-series modeling
- artificial intelligence for sensor signal analysis
- network science for time-series analysis
- physiological and biomedical signal analysis
- brain-computer interfaces and their applications
- industrial and IoT sensor time series
- forecasting and control with sensor networks
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