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2019 | OriginalPaper | Buchkapitel

Survey of Methods for Time Series Symbolic Aggregate Approximation

verfasst von : Lin Wang, Faming Lu, Minghao Cui, Yunxia Bao

Erschienen in: Data Science

Verlag: Springer Singapore

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Abstract

Time 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, which plays an important role in time series classification and clustering, pattern matching, anomaly detection and others. In this paper, existing symbolization representation methods of time series were reviewed and compared. Firstly, the classical symbolic aggregate approximation (SAX) principle and its deficiencies were analyzed. Then, several SAX improvement methods, including aSAX, SMSAX, ESAX and some others, were introduced and classified; Meanwhile, an experiment evaluation of the existing SAX methods was given. Finally, some unresolved issues of existing SAX methods were summed up for future work.

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Metadaten
Titel
Survey of Methods for Time Series Symbolic Aggregate Approximation
verfasst von
Lin Wang
Faming Lu
Minghao Cui
Yunxia Bao
Copyright-Jahr
2019
Verlag
Springer Singapore
Buch
Data Science

Print ISBN: 978-981-15-0117-3

Electronic ISBN: 978-981-15-0118-0

Copyright-Jahr: 2019

DOI
https://doi.org/10.1007/978-981-15-0118-0_50

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