A dispersion-based PAA representation for time series

2009 WRI World Congress on Computer Science and Information Engineering, CSIE 2009

Volumn 4, Issue , 2009, Pages 490-494

  Karamitopoulos, Leonidas ^a   Evangelidis, Georgios ^a  

^a UNIVERSITY OF MACEDONIA   (Greece)

Author keywords

[No Author keywords available]

Indexed keywords

DATA SETS; HIGH DIMENSIONALITY; PIECEWISE AGGREGATE APPROXIMATION; STANDARD DEVIATION; TIME-SERIES DATA;

COMPUTER SCIENCE; DISPERSIONS;

TIME SERIES;

EID: 71049156242     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CSIE.2009.622     Document Type: Conference Paper

References (14)

1. R. Agrawal, C. Faloutsos, and A. Swami. Efficient similarity search in sequence databases. In Proc. 4th Int. Conf. FODO, pages 69-84, Evanston, IL, USA, October 1993.
2. D. Berndt and J. Clifford. Using dynamic time warping to find patterns in time series. In Proc. AAAI Workshop on Knowledge Discovery in Databases, pages 229-248, 1994.
3. K. Chan and A. W. Fu. Efficient time series matching by wavelets. In Proc. ICDE, pages 126-133, Sydney, Australia, March 1999.
4. D. Gunopoulos and G. Das. Time series similarity measures, tutorial notes. In Proc. ACM SIGKDD, pages 243-307, Boston, MA, USA, August 2000.
5. T. Hastie, R. Tibshirani, and J. Friedman. The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Springer, Canada, 2001.
6. E. Keogh. http://www.cs.ucr.edu/~eamonn/time-series-data.
7. E. Keogh, K. Chakrabarti, M. Pazzani, and S. Mehrotra. Dimensionality reduction for fast similarity search in large time series databases. Knowledge and Information Systems, 3(3):263-286, February 2001.
8. E. Keogh and S. Kasetty. On the need for time series data mining benchmarks: A survey and empirical demonstration. Data Mining and Knowledge Discovery, Springer Netherlands, 7(4):349-371, October 2003.
9. F. Korn, H. Jagadish, and C. Faloutsos. Efficiently supporting ad hoc queries in large datasets of time sequences. In Proc. ACM SIGMOD, pages 289-300, Tucson, AZ, USA, May 1997.
10. J. Lin, E. Keogh, S. Lonardi, and B. Chiu. A symbolic representation of time series, with implications for streaming algorithms. In Proc. DMKD, pages 2-11, San Diego CA, USA, June 2000.
11. B. Lkhagva, Y. Suzuki, and K. Kawagoe. New time series data representation esax for financial applications. In Proc. ICDEW, pages 115-117, Atlanta, GA, USA, April 2006.
12. A. Nanopoulos, R. Alcock, and Y. Manolopoulos. Feature-based classification of time-series data. In N. Mastorakis and S. D. Nikolopoulos, editors, Information processing and technology, pages 49-61. Nova Science Publishers, Inc., Commack, NY, USA, 2001.
13. H. Shatkay. Approximate queries and representations for large data sequences. Technical Report cs-95-03, Department of Computer Science, Brown University, 1995.
14. B. K. Yi and C. Faloutsos. Fast time sequence indexing for arbitrary lp norms. In Proc. VLDB, pages 385-394, Cairo, Egypt, September 2000.