Estimating the number of segments in time series data using permutation tests

Proceedings - IEEE International Conference on Data Mining, ICDM

Volumn , Issue , 2002, Pages 466-473

  Vasko, Kari T ^a   Toivonen, Hannu T T ^a,b  

^a UNIVERSITY OF HELSINKI   (Finland)

^b NOKIA RESEARCH CENTER   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 1642433203     PISSN: 15504786     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (17)

1. H. Akaike. A new look at the statistical model identification. IEEE Transactions on Automatic Control, 19(6):716-723, 1974.
2. F. Arnold. Mathematical Statistics. Prentice-Hall, New Jersey, 1990.
3. A. Barron, J. Rissanen, and B. Yu. The minimum description length principle in coding and modeling. IEEE Transactions on Information Theory, 44(6):2743-2760, 1998.
4. K. Bennett. Determination of the number of zones in a biostratigraphical sequence. New Phytol., 132:155-170, 1996. (Pubitemid 3023222)
5. X. Ge and P. Smyth. Segmental Semi-Markov models for endpoint detection in plasma etching. Technical Report 00-08, UCI-ICS, 2000.
6. J. Himberg, K. Korpiaho, H. Mannila, J. Tikanmäki, and H. T. Toivonen. Time series segmentation for context recognition in mobile devices. In The 2001 IEEE International Conference on Data Mining (ICDM'01), pages 203-210, San Jose, California, November-December 2001.
7. E. Keogh, S. Chu, D. Hart, and M. Pazzani. An online algorithm for segmenting time series. In IEEE International Conference on Data Mining, pages 289-296, 2001.
8. E. Keogh and P. Smyth. A probabilistic approach to fast pattern matching in time series databases. In Proceedings of the Third International Conference on Knowledge Discovery and Data Mining (KDD'97), pages 20-24. AAAI Press, 1997.
9. A. Korhola, K. Vasko, H. Toivonen, and H. Olander. Holocene temperature changes in northern Fennoscandia reconstructed from chironomids using Bayesian modelling. Quaternary Science Review, 21:1841-1860, 2002.
10. R. MacArthur. On the relative abundance of bird species. Proceedings of the National Academy of Science, 43:293-295, 1957.
11. H. Mannila, H. Toivonen, A. Korhola, and H. Olander. Learning, mining, or modeling? A case study in paleoecology. In Discovery Science, First International Conference, Lecture Notes in Artificial Intelligence 1532, pages 12-24, Fukuoka, Japan, 1998. Springer-Verlag.
12. G. Schwarz. Estimating the dimension of a model. The Annals of Statistics, 7(2):461-464, 1978.
13. H. Shatkay and S. B. Zdonik. Approximate queries and representations for large data sequences. In Proceedings of the 12th International Conference on Data Engineering, pages 536-545, Washington - Brussels - Tokyo, Feb. 1996. IEEE Computer Society.
14. P. Smyth. Clustering using Monte Carlo cross-validation. In Second International Conference on Knowledge Discovery and Data Mining, pages 126-133, Portland, Oregon, USA, 1996.
15. P. Smyth. Model selection for probabilistic clustering using cross-validated likelihood. Technical Report UCI-ICS 98-09, Information and Computer Science, University of California, Irvine, CA, 1998.
16. M. Sugiyama and H. Ogawa. Subspace information criterion for model selection. Neural Computation, 13(8):1863-1889, 2001.
17. V. Vapnik. The Nature of Statistical Learning Theory. Springer-Verlag, N.Y., 1995.