Compression of time series by extracting major extrema

Journal of Experimental and Theoretical Artificial Intelligence

Volumn 23, Issue 2, 2011, Pages 255-270

  Fink, Eugene ^a   Gandhi, Harith Suman ^a  

^a Carnegie Mellon University   (United States)

Author keywords

lossy compression; major minima and maxima; time series

Indexed keywords

LOSSY COMPRESSIONS; MAJOR MINIMA AND MAXIMA; MATHEMATICAL PROPERTIES;

TIME SERIES;

EID: 79957818369     PISSN: 0952813X     EISSN: 13623079     Source Type: Journal    
DOI: 10.1080/0952813X.2010.505800     Document Type: Article

References (36)

1. Agrawal, R., Psaila, G., Wimmers, E.L., and Zait, M. (1995), 'Querying Shapes of Histories', in Proceedings of the Twenty-First International Conference on Very Large Data Bases, pp. 502-514.
2. André-Jönsson, H., and Badal, D.Z. (1997), 'Using Signature Files for Querying Time-Series Data', in Proceedings of the First European Symposium on Principles of Data Mining and Knowledge Discovery, pp. 211-220. (Pubitemid 127097506)
3. Box, G.E.P., Jenkins, G.M., and Reinsel, G.C. (2008), Time Series Analysis: Forecasting and Control (4th ed.), Hoboken, NJ: John Wiley & Sons.
4. Burrus, C.S., Gopinath, R.A., and Guo, H. (1997), Introduction to Wavelets and Wavelet Transforms: A Primer, Englewood Cliffs, NJ: Prentice Hall.
5. Chan, F.K.-P., and Fu, W.A. (1999), 'Efficient Time Series Matching by Wavelets', in Proceedings of the Fifteenth International Conference on Data Engineering, pp. 126-133.
6. Chan, F.K.-P., Fu, W.A., and Yu, C. (2003), 'Haar Wavelets for Efficient Similarity Search of Time-series: With and Without Time Warping', IEEE Transactions of Knowledge and Data Engineering, 15, 686-705.
7. Cryer, J.D., and Chan, K.-S. (2009), Time Series Analysis: With Applications in R (2nd ed.), Berlin: Springer.
8. Das, G., Lin, K.-I., Mannila, H., Renganathan, G., and Smyth, P. (1998), 'Rule Discovery from Time Series', in Proceedings of the Fourth ACM International Conference on Knowledge Discovery and Data Mining, pp. 16-22.
9. Di, S., Jin, H., Li, S., Tie, J., and Chen, L. (2007), 'Efficient Time Series Data Classification and Compression in Distributed Monitoring', in Emerging Technologies in Knowledge Discovery and Data Mining, eds. T. Washio, Z.-H. Zhou, J.Z. Huang, X. Hu, J. Li, C. Xie, J. He, D. Zou, K.-C. Li, and M.M. Freire, Berlin: Springer, pp. 389-400. (Pubitemid 351153010)
10. Dorr, D.H., and Denton, A.M. (2009), 'Establishing Relationships among Patterns in Stock Market Data', Data and Knowledge Engineering, 68, 318-337.
11. Eruhimov, V., Martyanov, V., Raulefs, P., and Tuv, E. (2006), 'Combining Unsupervised and Supervised Approaches to Feature Selection for Multivariate Signal Compression', in Intelligent Data Engineering and Automated Learning, eds. E. Corchado, H. Yin, V. Botti, and C. Fyfe, Berlin: Springer, pp. 480-487. (Pubitemid 44670406)
12. Fink, E., and Pratt, K.B. (2003), 'Indexing of Compressed Time Series', in Data Mining in Time Series Databases, eds. M. Last, A. Kandel, and H. Bunke, Singapore: World Scientific Publishing, pp. 51-78.
13. Fu, T.-C., Chung, F.-L., Luk, R., and Ng, C.-M. (2008), 'Representing Financial Time Series based on Data Point Importance', Engineering Applications of Artificial Intelligence, 21, 277-300. (Pubitemid 351381576)
14. Gandhi, H.S. (2004), 'Important Extrema of Time Series: Theory and Applications', unpublished Master's thesis, University of South Florida, Department of Computer Science and Engineering.
15. Graps, A.L. (1995), 'An Introduction to Wavelets', IEEE Computational Science and Engineering, 2, 50-61.
16. Guralnik, V., and Srivastava, J. (1999), 'Event Detection from Time Series Data', in Proceedings of the Fifth ACM SIGMOD International Conference on Knowledge Discovery and Data Mining, pp. 33-42.
17. Guralnik, V., Wijesekera, D., and Srivastava, J. (1998), 'Pattern-directed Mining of Sequence Data', in Proceedings of the Fourth ACM International Conference on Knowledge Discovery and Data Mining, pp. 51-57.
18. Gusfield, D. (1997), Algorithms on Strings, Trees, and Sequences: Computer Science and Computational Biology, Cambridge: Cambridge University Press.
19. Huang, Y.-W., and Yu, P.S. (1999), 'Adaptive Query Processing for Time-Series Data', in Proceedings of the Fifth International Conference on Knowledge Discovery and Data Mining, pp. 282-286.
20. Ikeda, K., Vaughn, B.V., and Quint, S.R. (1999), 'Wavelet Decomposition of Heart Period Data', in Proceedings of the IEEE First Joint BMES/EMBS Conference, pp. 3-11.
21. Keogh, E.J., Chu, S., Hart, D., and Pazzani, M.J. (2001), 'An Online Algorithm for Segmenting Time Series', in Proceedings of the IEEE International Conference on Data Mining, pp. 289-296.
22. Keogh, E.J., and Pazzani, M.J. (1998), 'An Enhanced Representation of Time Series which Allows Fast and Accurate Classification, Clustering and Relevance Feedback', in Proceedings of the Fourth ACM International Conference on Knowledge Discovery and Data Mining, pp. 239-243.
23. Kriegel, H.-P., Kröger, P., Pryakhin, A., and Renz, M. (2008), 'Analysis of Time Series using Compact Model-based Descriptions', in Database Systems for Advanced Applications, eds. J.R. Haritsa, R. Kotagiri, and V. Pudi, Berlin: Springer, pp. 698-701.
24. Lam, S.K., and Wong, M.H. (1998), 'A Fast Projection Algorithm for Sequence Data Searching', Data and Knowledge Engineering, 28, 321-339. (Pubitemid 128398285)
25. Last, M., Klein, Y., and Kandel, A. (2001), 'Knowledge Discovery in Time Series Databases', IEEE Transactions on Systems, Man, and Cybernetics, Part B, 31, 160-169. (Pubitemid 32300856)
26. Lin, L., and Risch, T. (1998), 'Querying Continuous Time Sequences', in Proceedings of the Twenty- Fourth International Conference on Very Large Data Bases, pp. 170-181.
27. Montgomery, D.C., Jennings, C.L., and Kulahci, M. (2008), Introduction to Time Series Analysis and Forecasting, Hoboken, NJ: John Wiley & Sons.
28. Park, S., Lee, D., and Chu, W.W. (1999), 'Fast Retrieval of Similar Subsequences in Long Sequence Databases', in Proceedings of the Third IEEE Knowledge and Data Engineering Exchange Workshop, pp. 60-67.
29. Perng, C.-S., Wang, H., Zhang, S.R., and Parker, S.D. (2000), 'Landmarks: A New Model for Similarity-based Pattern Querying in Time Series Databases', in Proceedings of the Sixteenth International Conference on Data Engineering, pp. 33-42. (Pubitemid 30585778)
30. Pratt, K.B. (2001), 'Locating Patterns in Discrete Time Series', Master's thesis, University of South Florida, Department of Computer Science and Engineering.
31. Pratt, K.B., and Fink, E. (2002), 'Search for Patterns in Compressed Time Series', International Journal of Image and Graphics, 2, 89-106.
32. Qu, Y., Wang, C., and Wang, X.S. (1998), 'Supporting Fast Search in Time Series for Movement Patterns in Multiple Scales', in Proceedings of the Seventh International Conference on Information and Knowledge Management, pp. 251-258.
33. Sheikholeslami, G., Chatterjee, S., and Zhang, A. (1998), 'WaveCluster: A Multi-resolution Clustering Approach for very Large Spatial Databases', in Proceedings of the Twenty-fourth International Conference on Very Large Data Bases, pp. 428-439.
34. Singh, S., and McAtackney, P. (1998), 'Dynamic Time-series Forecasting using Local Approximation', in Proceedings of the Tenth IEEE International Conference on Tools with Artificial Intelligence, pp. 392-399.
35. Stoffer, D.S. (1999), 'Detecting Common Signals in Multiple Time Series using the Spectral Envelope', Journal of the American Statistical Association, 94, 1341-1356.
36. Yi, B.-K., Sidiropoulos, N.D., Johnson, T., Jagadish, H.V., Faloutsos, C., and Biliris, A. (2000), 'Online Data Mining for Co-evolving Time Series', in Proceedings of the Sixteenth International Conference on Data Engineering, pp. 13-22. (Pubitemid 30585780)