Time Series Analysis in Sensor Fusion
A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Sensor Networks".
Deadline for manuscript submissions: closed (29 February 2024) | Viewed by 623
Special Issue Editors
Interests: multidimensional systems; signal processing; machine learning; time series analysis
Interests: remote image; computer vision; biomedical analysis
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
With the rapid development of sensing technology, a large volume of time series data has been collected, often by several sensors, in diverse applications such as financial markets, weather forecasting, utility demand prediction, fault detection in manufacturing, agriculture applications, and health monitoring. As time series data contains valuable, but often hidden, information, it is important to process it by exploiting data analytics, signal processing, and machine learning. Therefore, it is timely to report on the recent progress in time series analysis in sensor fusion.
This Special Issue aims to present recent advances in time series analysis in sensor fusion. It will include, but is not limited to, papers discussing time series with multiple sensors relating the following topics:
- Novel methods for combining data from multiple sensors;
- Machine learning and statistical techniques;
- Signal processing techniques;
- Feature extraction and selection;
- Temporal alignment and calibration;
- Time series forecasting and prediction;
- Anomaly detection and fault diagnosis;
- Data fusion for localization and tracking;
- Application-specific studies;
- Privacy and security considerations.
Dr. Zhiping Lin
Prof. Dr. Fulin Luo
Guest Editors
Manuscript Submission Information
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Keywords
- time series
- machine learning
- signal processing
- feature extraction
- temporal alignment and calibration
- forecasting and prediction
- anomaly detection
- localization and tracking
- sensor fusion
