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2019 | OriginalPaper | Chapter

Survey of Methods for Time Series Symbolic Aggregate Approximation

Authors : Lin Wang, Faming Lu, Minghao Cui, Yunxia Bao

Published in: Data Science

Publisher: 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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Literature
1.
Annam, J.R., Surampudi, B.R.: Inter-patient heart-beat classification using complete ECG beat time series by alignment of R-peaks using SVM and decision rule. In: International Conference on Signal & Information Processing. (2017)
2.
Yang, P., Dumont, G., Ansermino, J.M.: Adaptive change detection in heart rate trend monitoring in anesthetized children. IEEE Trans. Biomed. Eng. 53(11), 2211–2219 (2006)CrossRef
3.
Rathnayaka, R.M.K.T., Seneviratne, D.M.K.N., Wei, J., Arumawadu, H.I.: A hybrid statistical approach for stock market forecasting based on artificial neural network and ARIMA time series models. In: International Conference on Behavioral (2015)
4.
Golmohammadi, K., Zaiane, O.R.: Time series contextual anomaly detection for detecting market manipulation in stock market. In: IEEE International Conference on Data Science & Advanced Analytics (2015)
5.
Papagiannopoulou, C., Decubber, S., Miralles, D.G., Demuzere, M., Verhoest, Niko E.C., Waegeman, W.: Analyzing granger causality in climate data with time series classification methods. In: Altun, Y., et al. (eds.) ECML PKDD 2017. LNCS (LNAI), vol. 10536, pp. 15–26. Springer, Cham (2017). https://​doi.​org/​10.​1007/​978-3-319-71273-4_​2CrossRef
6.
Itoh, N., Kurths, J.: Change-point detection of climate time series by nonparametric method. In: Proceedings of the World Congress on Engineering and Computer Science, vol. 1, pp. 445–448 (2010)
7.
Xing, W., Lin, J., Patel, N., Braun, M.: A self-learning and online algorithm for time series anomaly detection, with application in CPU manufacturing. In: ACM International on Conference on Information & Knowledge Management (2016)
8.
Feng, C., Li, T., Chana, D.: Multi-level anomaly detection in industrial control systems via package signatures and LSTM networks. In: IEEE/IFIP International Conference on Dependable Systems & Networks (2017)
9.
10.
Chan, K.P., Fu, W.C.: Efficient time series matching by wavelets. In: International Conference on Data Engineering (1999)
11.
Keogh, E., Chu, S., Hart, D., Pazzani, M.: An online algorithm for segmenting time series. In: Proceedings 2001 IEEE International Conference on Data Mining, pp. 289–296. IEEE (2001)
12.
Keogh, E., Chakrabarti, K., Pazzani, M., Mehrotra, S.: Dimensionality reduction for fast similarity search in large time series databases. Knowl. Inf. Syst. 3(3), 263–286 (2001)CrossRef
13.
Lin, J., Keogh, E., Lonardi, S., Chiu, B.: A symbolic representation of time series, with implications for streaming algorithms. In: ACM Sigmod Workshop on Research Issues in Data Mining & Knowledge Discovery (2003)
14.
Faloutsos, C., Ranganathan, M., Manolopoulos, Y.: Fast subsequence matching in time-series databases. ACM (1994)
15.
Hugueney, B.: Adaptive segmentation-based symbolic representations of time series for better modeling and lower bounding distance measures. In: Fürnkranz, J., Scheffer, T., Spiliopoulou, M. (eds.) PKDD 2006. LNCS (LNAI), vol. 4213, pp. 545–552. Springer, Heidelberg (2006). https://​doi.​org/​10.​1007/​11871637_​54CrossRef
16.
Keogh, E., Chakrabarti, K., Pazzani, M., Mehrotra, S.: Locally adaptive dimensionality reduction for indexing large time series databases. ACM SIGMOD Rec. 30(2), 151–162 (2001)CrossRef
17.
Pham, N.D., Le, Q.L., Dang, T.K.: Two novel adaptive symbolic representations for similarity search in time series databases. In: 2010 12th International Asia-Pacific Web Conference, pp. 181–187. IEEE (2010)
18.
19.
陈湘涛, 李明亮, 陈玉娟: 基于分割模式的时间序列矢量符号化算法. 计算机工程 37(4), 55–57 (2011)
20.
21.
Lkhagva, B., Suzuki, Y., Kawagoe, K.: Extended SAX: extension of symbolic aggregate approximation for financial time series data representation. DEWS2006 4A-i8 7 (2006)
22.
Zhong, Q., Cai, Z.: Symbolic algorithm for time series data based on statistic feature. Chin. J. Comput. 31(10), 1857–1864 (2008)MathSciNetCrossRef
23.
李海林, 郭崇慧: 基于形态特征的时间序列符号聚合近似方法. 模式识别与人工智能 24(5), 665–672 (2011)
24.
Sun, Y., Li, J., Liu, J., Sun, B., Chow, C.: An improvement of symbolic aggregate approximation distance measure for time series. Neurocomputing 138, 189–198 (2014)CrossRef
25.
季海娟, 周从华, 刘志锋: 一种基于始末距离的时间序列符号聚合近似表示方法. 计算机科学 45(06), 222–227 (2018)
26.
李海林, 梁叶: 基于数值符号和形态特征的时间序列相似性度量方法. 控制与决策 32(3), 451–458 (2017)
27.
Metadata
Title
Survey of Methods for Time Series Symbolic Aggregate Approximation
Authors
Lin Wang
Faming Lu
Minghao Cui
Yunxia Bao
Copyright Year
2019
Publisher
Springer Singapore
Book
Data Science

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

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

Copyright Year: 2019

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

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