On the need for time series data mining benchmarks: A survey and empirical demonstration

Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

Volumn , Issue , 2002, Pages 102-111

  Keogh, Eamonn ^a   Kasetty, Shruti ^a  

^a University of California Riverside   (United States)

Author keywords

Data mining; Experimental evaluation; Time series

Indexed keywords

APPROXIMATION THEORY; DATABASE SYSTEMS; MATHEMATICAL MODELS; PIECEWISE LINEAR TECHNIQUES; TIME SERIES ANALYSIS;

DATABASE MANAGEMENT; SIMILARITY MEASURE;

DATA MINING;

EID: 0242709395     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (62)

1. Agrawal, R., Faloutsos, C. & Swami, A. (1993). Efficient similarity search in sequence databases. In proceedings of the 4th Int'l Conference on Foundations of Data Organization and Algorithms. Chicago, IL, Oct 13-15. pp. 69-84.
2. Agrawal, R., Lin, K. I., Sawhney, H. S. & Shim, K. (1995). Fast similarity search in the presence of noise, scaling, and translation in time-series databases. In proceedings of the 21st Int'l Conference on Very Large Databases. Zurich, Switzerland, Sept. pp 490-50.
3. Agrawal, R., Psaila, G., Wimmers, E. L. & Zait, M. (1995). Querying shapes of histories. In proceedings of the 21st Int'l Conference on Very Large Databases. Zurich, Switzerland, Sept 11-15. pp 502-514.
4. André-Jönsson, H. & Badal, D. (1997). Using signature files for querying time-series data. In proceedings of Principles of Data Mining and Knowledge Discovery, 1st European Symposium. Trondheim, Norway, Jun 24-27. pp 211-220.
5. Bailey, D. (1991). Twelve ways to fool the masses when giving performance results on parallel computers. Supercomputing Review, Aug. 1991, pp. 54-55.
6. Bay, S. (1999). UCI Repository of Kdd databases [http://kdd.ics.uci.edu/] Irvine, CA: University of California, Department of Information and Computer Science
7. Berndt, D. J. & Clifford, J. (1996). Finding patterns in time series: a dynamic programming approach. Advances in Knowledge Discovery and Data Mining. AAAI/MIT Pres, Menlo Park, CA. pp 229-248.
8. Bozkaya, T., Yazdani, N. & Ozsoyoglu, Z. M. (1997). Matching and indexing sequences of different lengths. In proceedings of the 6th Int'l Conference on Information and Knowledge Management. Las Vegas, NV, Nov 10-14. pp 128-135.
9. Caraça-Valente, J. P. & Lopez-Chavarrias, I. (2000). Discovering similar patterns in time series. In proceedings of the 6th ACM SIGKDD Int'l Conference on Knowledge Discovery and Data mining. Boston, MA, Aug 20-23. pp 497-505.
10. Chan, K. & Fu, A. W. (1999). Efficient time series matching by wavelets. In proceedings of the 15th IEEE Int'l Conference on Data Engineering. Sydney, Australia, Mar 23-26. pp 126-133.
11. Chu, K. & Wong, M. (1999). Fast time-series searching with scaling and shifting. In proceedings of the 18th ACM Symposium on Principles of Database Systems. Philadelphia, PA, May 31 - Jun 2. pp 237-248.
12. Cohen, W. (1993). Efficient pruning methods for separate-and-conquer rule learning systems. In proceedings of the 13th International Joint Conference on Artificial Intelligence, Chambery, France. pp 88-994.
13. Das, G., Gunopulos, D. & Mannila, H. (1997). Finding similar time series. In proceedings of Principles of Data Mining and Knowledge Discovery, 1st European Symposium. Trondheim, Norway, Jun 24-27. pp 88-100.
14. Das, G., Lin, K., Mannila, H., Renganathan, G. & Smyth, P. (1998). Rule discovery from time series. In proceedings of the 4th Int'l Conference on Knowledge Discovery and Data Mining. New York, NY, Aug 27-31. pp 16-22.
15. Debregeas, A. & Hebrail, G. (1998). Interactive interpretation of kohonen maps applied to curves. In proceedings of the 4th Int'l Conference of Knowledge Discovery and Data Mining. New York, NY, Aug 27-31. pp 179-183.
16. Faloutsos, C., Jagadish, H., Mendelzon, A. & Milo, T. (1997). A signature technique for similarity-based queries. In proceedings of the Int'l Conference on Compression and Complexity of Sequences. Positano-Salerno, Italy, Jun 11-13.
17. Faloutsos, C., Ranganathan, M. & Manolopoulos, Y. (1994). Fast subsequence matching in time-series databases. In proceedings of the ACM SIGMOD Int'l Conference on Management of Data. Minneapolis, MN, May 25-27. pp 419-429.
18. Ferhatosmanoglu, H., Tuncel, E., Agrawal, D. & El Abbadi, A. (2001). Approximate nearest neighbor searching in multimedia databases. In proceedings of the 17th IEEE Int'l Conference on Data Engineering. Heidelberg, Germany, Apr 2-6. pp 503-511.
19. Gavrilov, M., Anguelov, D., Indyk, P. & Motwani, R. (2000). Mining the stock market: which measure is best? In proceedings of the 6th ACM Int'l Conference on Knowledge Discovery and Data Mining. Boston, MA, Aug 20-23. pp 487-496.
20. Ge, X. & Smyth, P. (2000). Deformable markov model templates for time-series pattern matching. In proceedings of the 6th ACM SIGKDD Int'l Conference on Knowledge Discovery and Data Mining. Boston, MA, Aug 20-23. pp 81-90.
21. Geurts, P. (2001). Pattern extraction for time series classification. In proceedings of Principles of Data Mining and Knowledge Discovery, 5th European Conference. Freiburg, Germany, Sept 3-5. pp 115-127
22. Goldin, D. & Kanellakis, P. (1995) On similarity queries for time-series data: constraint specification and implementation. In proceedings of the 1st Int'l Conference on the Principles and Practice of Constraint Programming. Cassis, France, Sept 19-22. pp 137-153.
23. Guralnik, V. & Srivastava, J. (1999). Event detection from time series data. In proceedings of the 5th ACM SIGKDD Int'l Conference on Knowledge Discovery and Data Mining. San Diego, CA, Aug 15-18. pp 33-42.
24. Huang, Y. & Yu, P. S. (1999). Adaptive query processing for time-series data. In proceedings of the 5th Int'l Conference on Knowledge Discovery and Data Mining. San Diego, CA, Aug 15-18. pp 282-286.
25. Huhtala, Y., Kärkkäinen, J. & Toivonen, H. (1999). Mining for similarities in aligned time series using wavelets. Data Mining and Knowledge Discovery: Theory, Tools, and Technology, SPIE Proceedings Series, Vol. 3695. Orlando, FL, Apr. pp 150-160.
26. Indyk, P., Koudas, N. & Muthukrishnan, S. (2000). Identifying representative trends in massive time series data sets using sketches. In proceedings of the 26th Int'l Conference on Very Large Data Bases. Cairo, Egypt, Sept 10-14. pp 363-372.
27. Kahveci, T. & Singh, A. (2001). Variable length queries for time series data. In proceedings of the 17th Int'l Conference on Data Engineering. Heidelberg, Germany, Apr 2-6. pp 273-282.
28. Kahveci, T., Singh, A. & Gurel, A. (2002). An efficient index structure for shift and scale invariant search of multi-attribute time sequences. In proceedings of the 18th Int'l Conference on Data Engineering. San Jose, CA, Feb 26-Mar 1. to appear.
29. Kalpakis, K., Gada, D. & Puttagunta, V. (2001). Distance measures for effective clustering of ARIMA time-series. In proceedings of the IEEE Int'l Conference on Data Mining. San Jose, CA, Nov 29-Dec 2. pp 273-280.
30. Keogh, E. & Pazzani, M. (1998). An enhanced representation of time series which allows fast and accurate classification, clustering and relevance feedback. In proceedings of the 4th Int'l Conference on Knowledge Discovery and Data Mining. New York, NY, Aug 27-31. pp 239-241.
31. Keogh, E. & Smyth, P. (1997). A probabilistic approach to fast pattern matching in time series databases. In proceedings of the 3rd Int'l Conference on Knowledge Discovery and Data Mining. Newport Beach, CA, Aug 14-17. pp 24-30.
32. Keogh, E., Chakrabarti, K., Pazzani, M. & Mehrotra, S. (2001). Locally adaptive dimensionality reduction for indexing large time series databases. In proceedings of ACM SIGMOD Conference on Management of Data. Santa Barbara, CA, May 21-24. pp 151-162.
33. Kibler, D., & Langley, P. (1988). Machine learning as an experimental science. In Proceedings of the 3rd European Working Session on Learning. pp. 81-92.
34. Kim, E., Lam, J. M. & Han, J. (2000). AIM: approximate intelligent matching for time series data. In proceedings of Data Warehousing and Knowledge Discovery, 2nd Int'l Conference. London, UK, Sep 4-6. pp 347-357.
35. Korn, F., Jagadish, H. & Faloutsos, C. (1997). Efficiently supporting ad hoc queries in large datasets of time sequences. In proceedings of the ACM SIGMOD Int'l Conference on Management of Data. Tucson, AZ, May 13-15. pp 289-300.
36. Lam, S. K. & Wong, M. H. (1998). A fast projection algorithm for sequence data searching. Data & Knowledge Engineering, Vol. 28(3). pp 321-339.
37. Lavrenko, V., Schmill, M., Lawrie, D., Ogilvie, P., Jensen, D. & Allan, J. (2000). Mining of concurrent text and time series. In proceedings of the 6th ACM SIGKDD Int'l Conference on Knowledge Discovery and Data Mining Workshop on Text Mining. Boston, MA, Aug 20-23. pp 37-44.
38. Lee, S., Chun, S., Kim, D., Lee, J. & Chung, C. (2000). Similarity search for multidimensional data sequences. In proceedings of the 16th Int'l Conference on Data Engineering. San Diego, CA, Feb 28-Mar 3. pp 599-608.
39. Li, C., Yu, P. S. & Castelli, V. (1998). MALM: a framework for mining sequence database at multiple abstraction levels. In proceedings of the 7th ACM CIKM Int'l Conference on Information and Knowledge Management. Bethesda, MD, Nov 3-7. pp 267-272.
40. Loh, W., Kim, S. & Whang, K. (2000). Index interpolation: an approach to subsequence matching supporting normalization transform in time-series databases. In proceedings of the 9th ACM CIKM Int'l Conference on Information and Knowledge Management. McLean, VA, Nov 6-11. pp 480-487.
41. Park, S., Chu, W. W., Yoon, J. & Hsu, C. (2000). Efficient searches for similar subsequences of different lengths in sequence databases. In proceedings of the 16th Int'l Conference on Data Engineering. San Diego, CA, Feb 28-Mar 3. pp 23-32.
42. Park, S., Kim, S. & Chu, W. W. (2001). Segment-based approach for subsequence searches in sequence databases. In proceedings of the 16th ACM Symposium on Applied Computing. Las Vegas, NV, Mar 11-14. pp 248-252.
43. Park, S., Lee, D. & Chu, W. W. (1999). Fast retrieval of similar subsequences in long sequence databases. In proceedings of the 3rd IEEE Knowledge and Data Engineering Exchange Workshop. Chicago, IL, Nov 7.
44. Polly, W. P. M. & Wong, M. H. (2001). Efficient and robust feature extraction and pattern matching of time series by a lattice structure. In proceedings of the 10th ACM CIKM Int'l Conference on Information and Knowledge Management. Atlanta, GA, Nov 5-10. pp 271-278.
45. Popivanov, I. & Miller, R. J. (2002). Similarity search over time series data using wavelets. In proceedings of the 18th Int'l Conference on Data Engineering. San Jose, CA, Feb 26-Mar 1. pp 212-221.
46. Pratt, K. B. & Fink, E. (2002). Search for patterns in compressed time series. Int'l Journal of Image and Graphics. to appear.
47. Prechelt. L. (1995). A quantitative study of neural network learning algorithm evaluation practices. In proceedings of the 4th Int'l Conference on Artificial Neural Networks. pp. 223-227.
48. Qu, Y., Wang, C. & Wang, X. S. (1998). Supporting fast search in time series for movement patterns in multiples scales. In proceedings of the 7th ACM CIKM Int'l Conference on Information and Knowledge Management. Bethesda, MD, Nov 3-7. pp 251-258.
49. Rafiei, D. & Mendelzon, A. O. (1998). Efficient retrieval of similar time sequences using DFT. In proceedings of the 5th Int'l Conference on Foundations of Data Organization and Algorithms. Kobe, Japan, Nov 12-13.
50. Refiei, D. (1999). On similarity-based queries for time series data. In proceedings of the 15th IEEE Int'l Conference on Data Engineering. Sydney, Australia, Mar 23-26. pp 410-417.
51. Shahabi, C., Tian, X. & Zhao, W. (2000). TSA-tree: a wavelet based approach to improve the efficiency of multi-level surprise and trend queries. In proceedings of the 12th Int'l Conference on Scientific and Statistical Database Management. Berlin, Germany, Jul 26-28. pp 55-68.
52. Shatkay, H. & Zdonik, S. (1996). Approximate queries and representations for large data sequences. In proceedings of the 12th IEEE Int'l Conference on Data Engineering. New Orleans, LA, Feb 26-Mar 1. pp 536-545.
53. Simon, J. L. (1994). What some puzzling problems teach about the theory of simulation and the use of resampling. The American Statistician, Vol. 48(4). Nov. pp 1-4.
54. Struzik, Z. & Siebes, A. (1999). The Haar wavelet transform in the time series similarity paradigm. In proceedings of Principles of Data Mining and Knowledge Discovery, 3rd European Conference. Prague, Czech Republic, Sept 15-18. pp 12-22.
55. Walker, J. (2001). HotBits: Genuine random numbers generated by radioactive decay. www.fourmilab.ch/hotbits/
56. Wang, C. & Wang, X. S. (2000). Multilevel filtering for high dimensional nearest neighbor search. In proceedings of ACM SIGMOD Workshop on Research Issues in Data Mining and Knowledge Discovery. Dallas, TX, May 14. pp 37-43.
57. Wang, C. & Wang, X. S. (2000). Supporting content-based searches on time series via approximation. In proceedings of the 12th Int'l Conference on Scientific and Statistical Database Management. Berlin, Germany, Jul 26-28. pp 69-81.
58. Wang, C. & Wang, X. S. (2000). Supporting subseries nearest neighbor search via approximation. In proceedings of the 9th ACM CIKM Int'l Conference on Information and Knowledge Management. McLean, VA, Nov 6-11. pp 314-321.
59. Wu, L., Faloutsos, C., Sycara, K. & Payne, T. R. (2000). FALCON: feedback adaptive loop for content-based retrieval. In proceedings of the 26th Int'l Conference on Very Large Data Bases. Cairo, Egypt, Sept 10-14. pp 297-306.
60. Wu, Y., Agrawal, D. & El Abbadi, A. (2000). A comparison of DFT and DWT based similarity search in time-series databases. In proceedings of the 9th ACM CIKM Int'l Conference on Information and Knowledge Management. McLean, VA, Nov 6-11. pp 488-495.
61. Yi, B. & Faloutsos, C. (2000). Fast time sequence indexing for arbitrary lp norms. In proceedings of the 26th Int'l Conference on Very Large Databases. Cairo, Egypt, Sept 10-14. pp 385-394.
62. Yi, B., Jagadish, H. & Faloutsos, C. (1998). Efficient retrieval of similar time sequences under time warping. In proceedings of the 14th Int'l Conference on Data Engineering. Orlando, FL, Feb 23-27. pp 201-20.