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A Novel Symbolic Aggregate Approximation for Time Series
Advances in Intelligent Systems and Computing, 2019Symbolic 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
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MSAX: Multivariate Symbolic Aggregate Approximation for Time Series Classification
Lecture Notes in Computer Science, 2020Time 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 ...
Susana Vinga +2 more
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An improvement of symbolic aggregate approximation distance measure for time series
Neurocomputing, 2014Abstract 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.
Jiuyong Li +2 more
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Recognition of Signed Expressions Using Symbolic Aggregate Approximation
Lecture Notes in Computer Science, 2014Complexity of sign language recognition system grows with growing word vocabulary. Therefore it is advisable to use units smaller than words. Such elements, called subunits, resemble phonemes in spoken language. They are concatenated to form word models.
Mariusz Oszust, Marian Wysocki
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Clustering of time series using hybrid symbolic aggregate approximation
2017 IEEE Symposium Series on Computational Intelligence (SSCI), 2017Clustering of time series is one of the best-known grand challenges in time series analysis because of its application potentialities and difficulty. It is like data clustering and the task of partitioning time series into several groups based on their similarities, such that time series in a cluster are similar and they are not similar to other ...
Keiichi Tamura, Takumi Ichimura
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Rolling element bearings diagnostics using the Symbolic Aggregate approXimation
Mechanical Systems and Signal Processing, 2015Abstract 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
Petros Karvelis +2 more
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Fault diagnosis of bearing based on Symbolic Aggregate approXimation and Lempel-Ziv
Measurement: Journal of the International Measurement Confederation, 2019Abstract Aiming at the problem that the traditional Lempel-Ziv encoding method can’t accurately reflect the bearing modulation information in bearing fault diagnosis, an improved Lempel-Ziv method based on Symbolic Aggregate approXimation is proposed to improve the coding accuracy and the computational efficiency of Lempel-Ziv indicator. According to
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