Results 131 to 140 of about 27,804 (170)

Rolling element bearings diagnostics using the Symbolic Aggregate approXimation

Mechanical Systems and Signal Processing, 2015
Abstract Rolling element bearings are a very critical component in various engineering assets. Therefore it is of paramount importance the detection of possible faults, especially at an early stage, that may lead to unexpected interruptions of the production or worse, to severe accidents. This research work introduces a novel, in the field of bearing
George Georgoulas, Petros Karvelis, Theodoros Loutas, Chrysostomos D. Stylios   +3 more
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An improvement of symbolic aggregate approximation distance measure for time series

Neurocomputing, 2014
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 is mapped from the average value of a segment, the SAX ignores important information in a segment, namely the trend of the value change in the segment.
Youqiang Sun, Jiuyong Li, Jixue Liu, Bingyu Sun, Christopher Chow   +4 more
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MSAX: Multivariate Symbolic Aggregate Approximation for Time Series Classification

Lecture Notes in Computer Science, 2020
Time Series (TS) analysis is a central research topic in areas such as finance, bioinformatics, and weather forecasting, where the goal is to extract knowledge through data mining techniques. Symbolic aggregate approximation (SAX) is a state-of-the-art method that performs discretization and dimensionality reduction for univariate TS, which are key ...
Manuel Anacleto, Susana Vinga, Alexandra M. Carvalho   +2 more
openaire   +3 more sources

A Novel Symbolic Aggregate Approximation for Time Series

Advances in Intelligent Systems and Computing, 2019
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 and misses important information in a segment, namely the trend of the value change in the segment.
Yufeng Yu, Yuelong Zhu, Dingsheng Wan, Huan Liu, Qun Zhao   +4 more
openaire   +3 more sources

Empirical study of symbolic aggregate approximation for time series classification

Intelligent Data Analysis, 2017
Symbolic Aggregate approximation (SAX) has been the de facto standard representation methods for knowledge discovery in time series on a number of tasks and applications. So far, very little work has been done in empirically investigating the intrinsic properties and statistical mechanics in SAX words.
Song, Wei, Wang, Zhiguang, Zhang, Fan, Ye, Yangdong, Fan, Ming   +4 more
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Symbolic aggregate approximation based data fusion model for dangerous driving behavior detection

Information Sciences, 2022
Jia Liu, Tianrui Li, Zhong Yuan, Wei Huang, Peng Xie, Qianqian Huang   +5 more
openaire   +3 more sources

Quantile Symbolic Aggregate approXimation: A guaranteed equiprobable SAX

Anais do XXXVIII Simpósio Brasileiro de Banco de Dados (SBBD 2023), 2023
Time 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, Joaquim Assunção, Leonardo Emmendorfer   +2 more
openaire   +1 more source

Human motion retrieval with symbolic aggregate approXimation

2012 24th Chinese Control and Decision Conference (CCDC), 2012
Motion 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, null Yichuang Luo, null Song Gao   +2 more
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Distribution-Wise Symbolic Aggregate ApproXimation (dwSAX)

2020
The 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 Rozinajova
openaire   +1 more source