Large-scale indexing, discovery, and ranking for the Internet of Things (IoT)
Network-enabled sensing and actuation devices are key enablers to connect real-world
objects to the cyber world. The Internet of Things (IoT) consists of the network-enabled …
objects to the cyber world. The Internet of Things (IoT) consists of the network-enabled …
An improvement of symbolic aggregate approximation distance measure for time series
Abstract Symbolic Aggregate approXimation (SAX) as a major symbolic representation has
been widely used in many time series data mining applications. However, because a symbol …
been widely used in many time series data mining applications. However, because a symbol …
A new automatic sleep staging system based on statistical behavior of local extrema using single channel EEG signal
Over the past decade, converging evidence from diverse studies has demonstrated that
sleep is closely associated with the mental and physical health, quality of life, and safety …
sleep is closely associated with the mental and physical health, quality of life, and safety …
1d-sax: A novel symbolic representation for time series
S Malinowski, T Guyet, R Quiniou… - … Symposium on Intelligent …, 2013 - Springer
Abstract SAX (Symbolic Aggregate approXimation) is one of the main symbolization
techniques for time series. A well-known limitation of SAX is that trends are not taken into …
techniques for time series. A well-known limitation of SAX is that trends are not taken into …
[HTML][HTML] A machine learning-based Anomaly Detection Framework for building electricity consumption data
A suboptimal management or system malfunction can often lead to abnormal energy
consumptions in buildings, which result in a significant waste of energy. For this reason, the …
consumptions in buildings, which result in a significant waste of energy. For this reason, the …
Exploring the diverse world of SAX-based methodologies
Abstract Symbolic Aggregate Approximation (SAX) is a widely used method for time series
data analysis, known for its ability to transform continuous data to discrete symbols. While …
data analysis, known for its ability to transform continuous data to discrete symbols. While …
Electric demand forecasting with neural networks and symbolic time series representations
This paper addresses the electric demand prediction problem using neural networks and
symbolization techniques. Symbolization techniques provide a time series symbolic …
symbolization techniques. Symbolization techniques provide a time series symbolic …
A new pattern representation method for time-series data
R Rezvani, P Barnaghi… - IEEE Transactions on …, 2019 - ieeexplore.ieee.org
The rapid growth of Internet of Things (IoT) and sensing technologies has led to an
increasing interest in time-series data analysis. In many domains, detecting patterns of IoT …
increasing interest in time-series data analysis. In many domains, detecting patterns of IoT …
A novel symbolic aggregate approximation for time series
Y Yu, Y Zhu, D Wan, H Liu, Q Zhao - Proceedings of the 13th International …, 2019 - Springer
Symbolic Aggregate approximation (SAX) is a classical symbolic approach in many time
series data mining applications. However, SAX only reflects the segment mean value feature …
series data mining applications. However, SAX only reflects the segment mean value feature …
Detection of covert timing channel based on time series symbolization
S Wu, Y Chen, H Tian, C Sun - IEEE Open Journal of the …, 2021 - ieeexplore.ieee.org
Covert Timing Channels (CTCs) is a technique to leak information. CTCs only modify inter-
arrival time sequence (IATs) between packets, consequently, traditional network security …
arrival time sequence (IATs) between packets, consequently, traditional network security …