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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Acknowledgement
This work was supported by the National Natural Science Foundation of China [grant numbers 61602279, 61472229]; Shandong Province Postdoctoral Innovation Project [grant number 201603056]; the Sci. & Tech. Development Fund of Shandong Province of China [grant number 2016ZDJS02A11 and Grant ZR2017MF027]; the SDUST Research Fund [grant number 2015TDJH102]; and the Fund of Oceanic telemetry Engineering and Technology Research Center, State Oceanic Administration (grant number 2018002).
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Wang, L., Lu, F., Cui, M., Bao, Y. (2019). Survey of Methods for Time Series Symbolic Aggregate Approximation. In: Cheng, X., Jing, W., Song, X., Lu, Z. (eds) Data Science. ICPCSEE 2019. Communications in Computer and Information Science, vol 1058. Springer, Singapore. https://doi.org/10.1007/978-981-15-0118-0_50
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DOI: https://doi.org/10.1007/978-981-15-0118-0_50
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