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Entropy-based Symbolic Aggregate Approximation Representation Method for Time Series
2020 IEEE 9th Joint International Information Technology and Artificial Intelligence Conference (ITAIC), 2020Symbolic Aggregate approXimation (SAX) is one of the most common dimensionality reduction approaches for time-series and has been widely employed in lots of domains, including motif discovery, time-series classification, and fast shapelets discovery. However, SAX only considers the average value of the segment but ignores other essential features. As a
Haowen Zhang, Yabo Dong, Duanqing Xu
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Trend-based symbolic aggregate approximation for time series representation
2018 Chinese Control And Decision Conference (CCDC), 2018Due to high dimensionality and large volume of big time series data, the existing analysis technologies are poor for processing the raw data. The symbolic aggregate approximation (SAX) is one of the most powerful tools to deal with big time series data via reducing dimensionality.
Ke Zhang, Yuan Li, Yi Chai, Lei Huang
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RA-SAX: Resource-Aware Symbolic Aggregate Approximation for Mobile ECG Analysis
2011 IEEE 12th International Conference on Mobile Data Management, 2011There is a growing focus on 24/7 cardiac monitoring that leverages state of the art mobile phones and commercial-off-the-shelf (COTS) wearable bio-sensors. While many signal processing techniques for mobile ECG analysis have been developed, these techniques tend to be computationally intensive. In this paper, we propose, develop and evaluate a resource-
Shonali Krishnaswamy +1 more
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Symbolic aggregate approximation based data fusion model for dangerous driving behavior detection
Information Sciences, 2022Zhong Yuan, Peng Xie
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Quantile Symbolic Aggregate approXimation: A guaranteed equiprobable SAX
Anais do XXXVIII Simpósio Brasileiro de Banco de Dados (SBBD 2023), 2023Time series are broadly present in science and industry. In specific scenarios, it is useful to classify series in order to gain knowledge regarding a specific range of values. In such cases, we often use symbolic representation, as it can reduce the data dimensionality creating representative symbols, making the data discrete and allowing specialized ...
Eduardo Silveira +2 more
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Distribution-Wise Symbolic Aggregate ApproXimation (dwSAX)
2020The Symbolic Aggregate approXimation algorithm (SAX) is one of the most popular symbolic mapping techniques for time series. It is extensively utilized in sequence classification, pattern mining, anomaly detection and many other data mining tasks. SAX as a powerful symbolic mapping technique is widely used due to its data adaptability.
Matej Kloska, Viera Rozinajová
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Parallel symbolic aggregate approximation and its application in intelligent fault diagnosis
Journal of Intelligent & Fuzzy Systems, 2023Fault diagnosis is of great significance for industrial equipment maintenance, and feature extraction is a key step of the entire diagnosis scheme. The symbolic aggregate approximation (SAX) is a popular feature extraction approach with great potential recently.
Dongfang Zhao 0011 +4 more
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Human motion retrieval with symbolic aggregate approXimation
2012 24th Chinese Control and Decision Conference (CCDC), 2012Motion capture data exhibits its complexity both spatially and temporally, which makes it a hard work to measure the similarities between human motions. We propose a motion data indexing and retrieval method based on self-organizing map and symbolic aggregate approximation.
null Qinkun Xiao +2 more
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A Musical Similarity Metric based on Symbolic Aggregate Approximation
2020 International Conference on Software, Telecommunications and Computer Networks (SoftCOM), 2020We have continued our work in the field of AI-driven music synthesis and have improved upon our previous 3-layer gated recurrent unit neural network, with results confirming higher accuracy and much smaller validation loss. In order to achieve this, we have designed a recurrent neural architecture that is more suited to learning the musical style of J.
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Survey of Methods for Time Series Symbolic Aggregate Approximation
2019Time series analysis is widely used in the fields of finance, medical, and climate monitoring. However, the high dimension characteristic of time series brings a lot of inconvenience to its application. In order to solve the high dimensionality problem of time series, symbolic representation, a method of time series feature representation is proposed ...
Lin Wang +3 more
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