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MSAX: Multivariate Symbolic Aggregate Approximation for Time Series Classification
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 ...
Manuel Anacleto +2 more
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A Novel Symbolic Aggregate Approximation for Time Series
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 +4 more
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Recognition of Signed Expressions Using Symbolic Aggregate Approximation
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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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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SAX2SEX: Gender Classification on 3D Faces using Symbolic Aggregate ApproXimation
2019 6th International Conference on Image and Signal Processing and their Applications (ISPA), 2019Gender classification is a demographic attribute that found an increasing amount of applications particularly in human-computer interaction, security access control and biometrics. The purpose of this paper is to investigate the feasibility of using time series for gender classification.
Samia Bentaieb +2 more
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Genetic Algorithms-Based Symbolic Aggregate Approximation
2012Time series data appear in a broad variety of economic, medical, and scientific applications. Because of their high dimensionality, time series data are managed by using representation methods. Symbolic representation has attracted particular attention because of the possibility it offers to benefit from algorithms and techniques of other fields in ...
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Empirical study of symbolic aggregate approximation for time series classification
Intelligent Data Analysis, 2017Symbolic 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 +4 more
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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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Analytical Chemistry, 2020
Nontargeted mass spectrometry (MS) is widely used in life sciences and environmental chemistry to investigate large sets of samples. A major problem for larger-scale MS studies is data gaps or missing values in aligned data sets. The main causes for these data gaps are the absence of the compound from the sample, issues related to chromatography or ...
Erik Müller +4 more
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Nontargeted mass spectrometry (MS) is widely used in life sciences and environmental chemistry to investigate large sets of samples. A major problem for larger-scale MS studies is data gaps or missing values in aligned data sets. The main causes for these data gaps are the absence of the compound from the sample, issues related to chromatography or ...
Erik Müller +4 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.
Youqiang Sun +4 more
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