A Novel Symbolic Representation Based on Fast Segmentation

Article Preview

Abstract:

Symbolic representation of time series has recently attracted a lot of research interest. This is a difficult problem because of the high dimensionality of the data, particularly when the length of the time series becomes longer. In this paper, we introduce a new symbolic representation based on fast segmentation, called the trend feature symbols approximation (TFSA). The experimental results show that compared to some method, the segmentation efficiency of TFSA is improved.

You might also be interested in these eBooks

Info:

Periodical:

Pages:

3456-3461

Citation:

Online since:

May 2014

Export:

Price:

Permissions CCC:

Permissions PLS:

Сopyright:

© 2014 Trans Tech Publications Ltd. All Rights Reserved

Share:

Citation:

* - Corresponding Author

[1] MARZAT J, PIET-LAHANIER H, DAMONGEOT F, WALTER E. Model-based fault diagnosis for aerospace systems: a survey[J]. Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering, (2012).

DOI: 10.1177/0954410011421717

Google Scholar

[2] ANGIULLI F, BASTA S, LODI S, SARTORI C. A Distributed Approach to Detect Outliers in Very Large Data Sets. In Proceedings of Euro-Par'10, 2010, pp: 329-340.

DOI: 10.1007/978-3-642-15277-1_32

Google Scholar

[3] TRAINA C, FILHO R, TRAINA A, VIEIRA M, FALOUTSOS C. The Omni Family of All-purpose Access Methods: A Simple and Effective Way to Make Similarity Search More Efficient, The VLDB Journal, 2007, vol. 16: 483-505.

DOI: 10.1007/s00778-005-0178-0

Google Scholar

[4] LEMBESSIS E, ANTONOPOULOS G, KING R, HALATSIS C, & TORRES J. CASSANDRA: an on-line expert system for fault prognosis. Proc. the 5th CIM Europe Conference on Computer Integrated Manufacturing, 1989: 371–377.

Google Scholar

[5] SCHWABACHER M.A. A survey of Data-Driven prognostics. AIAA Infotech@Aerospace Conference, (2005).

DOI: 10.2514/6.2005-7002

Google Scholar

[6] DAVID L. I. Data Mining Applications for Space Mission Operations System Health Monitoring, NASA Ames Research Center, Moffett Field, California, 94035, (2008).

Google Scholar

[7] CHANDOLA V, BANERJEE A, KUMAR V. Anomaly detection: A survey. ACM Computing Surveys, 2009, 41(3): 1-58.

DOI: 10.1145/1541880.1541882

Google Scholar

[8] PARK H, MACKEY R, JAMES M, ZAK M, KYNARD M, SEBGHATI J, and GREENE W. Analysis of Space Shuttle Main Engine Data Using Beacon-based Exception Analysis for Multi- Missions. Proceedings of the IEEE Aerospace Conference, IEEE, New York, Vol. 6, March 9-16, 2002: 6-2835 - 6-2844.

DOI: 10.1109/aero.2002.1036123

Google Scholar

[9] SCHWABACHER M. Machine Learning for Rocket Propulsion Health Monitoring. Proceedings of the SAE World Aerospace Congress, Dallas, TX, (2005).

Google Scholar

[10] DAS K, SCHNEIDER J. Detecting anomalous records in categorical datasets. In KDD'07: Proceedings of the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2007, pp: 220-229.

DOI: 10.1145/1281192.1281219

Google Scholar

[11] DAVID L. Inductive System Health Monitoring. Proceedings of the International Conference on Artificial Intelligence, IC-AI 04, Volume 2 & Proceedings of the International Conference on Machine Learning; Models, Technologies & Applications, MLMTA , 04, June 21-24, 2004, Las Vegas, Nevada, USA.

Google Scholar

[12] Berndt Donald J, Clifford James. Using dynamic time warping to find patterns in time series[C]. In Proceedings of the KDD Workshop, Seattle, WA. 1994: 359-370.

Google Scholar

[13] KLEMA J, NOVAKOVA L, KAREL F, STEPANKOVA O. Sequential data mining: A comparative case study in development of atherosclerosis risk factors, IEEE Trans. Syst., Man, Cybern. C, Appl. Rev., 2008, vol. 38, no. 1: 3-15.

DOI: 10.1109/tsmcc.2007.906055

Google Scholar

[14] FINK E, PRATT K. B, GANDHI H.S. Indexing of Time Series by Major Minima and Maxima. Proc of the IEEE Int Conf on Systems, Man, and Cybernetics. Washington. DC: IEEE, 2003: 2332-2335.

DOI: 10.1109/icsmc.2003.1244232

Google Scholar

[15] BUDALAKOTI S, SRIVASTAVA A, AKELLA R. Discovering atypical flights in sequences of discrete flight parameters, in Proc. 2006, IEEE Aerospace. Conf., pp: 1-8.

DOI: 10.1109/aero.2006.1656109

Google Scholar

[16] RAMASWAMY S, RASTOGI R, SHIM K. Efficient Algorithms for Mining Outliers from Large Data Sets. SIGMOD Rec., 2000, 29(2): 427-438.

DOI: 10.1145/335191.335437

Google Scholar