Fast and exact warping of time series using adaptive segmental approximations

Machine Learning

Volumn 58, Issue 2-3, 2005, Pages 231-267

  Shou, Yutao ^a   Mamoulis, Nikos ^a   Cheung, David ^a  

^a UNIVERSITY OF HONG KONG   (Hong Kong)

Author keywords

Nearest neighbor Search; Similarity search; Time series analysis

Indexed keywords

ADAPTIVE SYSTEMS; ALGORITHMS; APPROXIMATION THEORY; DATA MINING; DATA REDUCTION; DATABASE SYSTEMS; SET THEORY;

DYNAMIC TIME WARPING (DTW); EUCLIDEAN DISTANCES; NEAREST NEIGHBOR SEARCH; SIMILARITY SEARCH;

TIME SERIES ANALYSIS;

EID: 15544379982     PISSN: 08856125     EISSN: None     Source Type: Journal    
DOI: 10.1007/s10994-005-5828-3     Document Type: Article

References (23)

1. Agrawal, R., Faloutsos, C., & Swami, A. (1993). Efficient similarity search in sequence databases. Proc. 4th Int. Conf. on Foundations of Data Organization and Algorithms (FODO) (pp. 69-84).
2. Beckmann, N., Kriegel, H.-P., Schneider, R., & Seeger, B. (1990). The R*-tree: An efficient and robust access method for points and rectangles. Proc. of the ACM SIGMOD Int. Conf. on Management of Data (pp. 322-331). Atlantic City, New Jersey.
3. Berndt, D. J. & Clifford, J. (1994). using dynamic time warping to find patterns in time series. Proc. of AAAI Workshop: Knowledge Discovery in Databases (pp. 359-370). Seattle, Washington.
4. Chu, S., Keogh, E., Hart, D., & Pazzani, M. (2002). Iterative deepening dynamic time warping for time series. In: Proc of SIAM International Conference on Data Mining.
5. Faloutsos, C., Ranganathan, M., & Manolopoulos, Y. (1994). Fast subsequence matching in time-series databases. Proc. of the ACM SIGMOD Int. Conf. on Management of Data (pp. 419-429). Minneapolis, Minnesota.
6. Goldin, D. & Kanellakis, P. (1995). On similarity queries for time-series data: constraint specification and implementation. Proc. of the 1st Intl Conference on the Principles and Practice of Constraint Programming (pp. 137-153). Cassis, France.
7. Hjaltason, G. & Samet, H. (1999). distance browsing in spatial databases. ACM Transactions on Database Systems 24:2, 265-318.
8. Kaufman, L. & Rousseeuw, P. (1990). Finding Groups in Data: An Introduction to Cluster Analysis. Wiley.
9. Keogh, E. (2002a). Exact indexing of dynamic time warping. Proc. of 28th International Conference on Very Large Data Bases (pp. 406-417). Hong Kong, China.
10. Keogh, E. (2002b). The UCR Time Series Data Mining Archive. Computer Science & Engineering Department, University of California, Riverside CA, http://www.cs.ucr.edu/~eamonn/TSDMA/index.html.
11. Keogh, E., Chakrabarti, K., Mehrotra, S., & Pazzani, M. (2001). Locally adaptive dimensionality reduction for indexing large time series databases. Proc. of the ACM SIGMOD Int. Conf. on Management of Data (pp. 151-162). Santa Barbara, California,.
12. Keogh, E. & Pazzani, M. (1999). scaling up dynamic time warping to massive datasets. Proc. of the Eur. Conf on Principles of Data Mining and Knowledge Discovery (PKDD) (pp. 1-11). Prague, Czech Republic.
13. Kim, S. W., Park, S., & Chu, W. W. (2001). An indexed-based approach for similarity search supporting time warping in large sequence databases. Proc. of the 17th International Conference on Data Engineering (pp. 607-614). Heidelberg, Germany.
14. Kruskall, J. B. & Liberman, M. (1983). The symmetric time warping algorithm: From continuous to discrete. In: Time Warps, String Edits and Macromolecules. Addison-Wesley.
15. Moon, Y. S., Whang, K. Y., & Han, W. S. (2002). GeneralMatch: A subsequence matching method in time-series databases based on generalized windows.Proc. of the ACM SIGMOD Int. Conf. on Management of Data (pp. 382-393). Madison, Wisconsin.
16. Munich, M. E. & Perona, P. (1999). continuous dynamic time warping for translation-invariant curve alignment with applications to signature verification. Proc. of the 8 th Int'l Conf. on Computer Vision (pp. 20-25). Corfu, Greece.
17. Rabiner, L. & Juang, B. H. (1993). Fundamentals of Speech Recognition. Prentice Hall.
18. Ratanamahatana, C. A. & Keogh, E. (2004). making time-series classification more accurate using learned constraints. Proc. of SIAM International Conference on Data Mining (SDM '04) (pp. 11-22). Lake Buena Vista, Florida.
19. Sakoe, H. & Chiba, S. (1978). dynamic programming algorithm optimization for spoken word recognition. IEEE Trans. Acoustics, Speech and Signal Processing 26:1, 43-49.
20. Seidl, T. & Kriegel, H.-P. (1998). Optimal multi-step k-nearest neighbor search. Proc. of the ACM SIGMOD Int. Conf. on Management of Data (pp. 154-165). Seattle, Washington.
21. Vlachos, M., Hadjieleftheriou, M., Gunopulos, D., & Keogh, E. (2003). indexing multi-dimensional time-series with support for multiple distance measures. Proc. of the 9th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 216-225). Washington, DC.
22. Yi, B., Jagadish, H., & Faloutsos, C. (1998). Efficient retrieval of similar time sequences under time warping. Proc. of the Fourteenth International Conference on Data Engineering. pp. 201-208, Orlando, Florida.
23. Zhu, Y. & Shasha, D. (2003). warping indexes with envelope transforms for query by humming. Proc. of the ACM SIGMOD Int. Conf. on Management of Data (pp. 181-192). San Diego, California.