SAXO: An optimized data-driven symbolic representation of time series

Proceedings of the International Joint Conference on Neural Networks

Volumn , Issue , 2013, Pages

  Bondu, A ^a   Boulle, M ^b   Grossin, B ^a  

^a EDF R AND D   (France)

^b ORANGE LABS   (France)

Author keywords

[No Author keywords available]

Indexed keywords

BAYESIAN; CO-CLUSTERING; DATA POINTS; GENERIC REPRESENTATION; LARGE AMOUNTS; SMART GRID; SYMBOLIC REPRESENTATION; TIME DIMENSION;

NEURAL NETWORKS; SMART POWER GRIDS;

TIME SERIES;

EID: 84893524664     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/IJCNN.2013.6706816     Document Type: Conference Paper

References (34)

1. A. Antoniadis, X. Brossat, J. Cugliari, and J. M. Poggi. Clustering functional data using wavelets. In COMPSTAT (International Conference on Computational Statistics), 2010.
2. A. Bondu and M. Boullé. A supervised approach for change detection in data streams. In IJCNN (International Joint Conference on Neural Networks), pages 519-526. IEEE, 2011.
3. A. Bondu, B. Grossin, and ML. Picard. Density estimation on data streams: An application to Change Detection. In EGC (Extraction et Gestion de l'Information), 2010.
4. A. Ba, Y. Goude, M Sinn, and P. Pompey. Adaptive Learning of Smoothing Functions: Application to Electricity Load Forecasting. In NIPS (Neural Information Processing Systems), 2012.
5. J. Lin, E. Keogh, S. Lonardi, and B. Chiu. A Symbolic Representation of Time Series, with Implications for Streaming Algorithms. In 8th ACM SIGMOD Workshop on Research Issues in Data Mining and Knowledge Discovery, San Diego, 2003.
6. D. Bosq. Linear Processes in Function Spaces: Theory and Applications (Lecture Notes in Statistics). Springer, 2000.
7. J.O. Ramsay and B.W. Silverman. Functional Data Analysis. Springer Series in Statistics. Springer, 2005.
8. R.E. Bellman. Adaptive Control Processes. Princeton University Press, Princeton, NJ, USA, 1961.
9. M. Frigo and S.G. Johnson. The design and implementation of FFTW3. Proceedings of the IEEE, 93(2):216- 231, 2005. Special issue on "Program Generation, Optimization, and Platform Adaptation".
10. R. Polikar. Physics and Modern Topics in Mechanical and Electrical Engineering, chapter The story of wavelets. World Scientific and Eng. Society Press, 1999.
11. K. Chan and W. Fu. Efficient Time Series Matching by Wavelets. In ICDE '99: Proceedings of the 15th International Conference on Data Engineering. IEEE Computer Society, 1999.
12. N. Ahmed, T. Natarajan, and K. R. Rao. Discrete Cosine Transfom. IEEE Trans. Comput., 23(1):90-93, 1974.
13. C. Guo, H. Li, and D. Pan. An improved piecewise aggregate approximation based on statistical features for time series mining. In Proceedings of the 4th international conference on Knowledge science, engineering and management, KSEM'10, pages 234-244, Berlin, Heidelberg, 2010. Springer-Verlag.
14. A. Gionis and H. Mannila. Finding Recurrent Sources in Sequences. In 7th International Conference on Research in Computational Molecular Biology, Berlin, 2003.
15. G. Reinert, S. Schbath, and M.S. Waterman. Probabilistic and Statistical Properties of Words: An Overview. Journal of Computational Biology, 7:1-46, 2002.
16. C.S. Daw, C.E.A. Finney, and E.R. Tracy. Symbolic Analysis of Experimental Data. Review of Scientific Instruments, 2002.
17. C. Ratanamahatana, E. Keogh, T. Bagnall, and S. Lonardi. A Novel Bit Level Time Series Representation with Implications for Similarity Search and Clustering. In PAKDD, 2005.
18. C. Cortes, K. Fisher, D. Pregibon, A. Rogers, and F. Smith. Hancock: A Language for Extracting Signatures from Data Streams. In 6th ACM SIGKDD Int'l Conference on Knowledge Discovery and Data Mining, pages 9-17, Boston, 2000.
19. E. Keogh, J. Lin, and A. Fu. HOT SAX: Efficiently Finding the Most Unusual Time Series Subsequence. In 5th IEEE International Conference on Data Mining, pages 226-233, Houston, Texas, 2005.
20. B. Chiu, E. Keogh, and S. Lonardi. Probabilistic Discovery of Time Series Motifs. In 9th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pages 493-498, Washington, 2003.
21. E. Keogh, L. Wei, X. Xi, S. Lonardi, J. Shieh, and S. Sirowy. Intelligent Icons: Integrating Lite-Weight Data Mining and Visualization into GUI Operating Systems. In International Conference on Data Mining, 2006.
22. A. Camerra, T. Palpanas, J. Shieh, and E. Keogh. iSAX 2.0: Indexing and Mining One Billion Time Series. In International Conference on Data Mining, 2010.
23. E. Keogh, K. Chakraborty, S. Mehrotra, and M. Pazzani. Dimensionality Reduction for Fast Similarity Search in Large Time Series Databases. Journal of Knowledge and Information Systems, 3:263-286, 2000.
24. B. K. Yi and C. Faloutsos. Fast time sequence indexing for arbitrary Lp norms. In 26th International Conference on Very Large Databases, Cairo, 2000.
25. E. Keogh and S. Kasetty. On the Need for Time Series Data Mining Benchmarks: A Survey and Empirical Demonstration. In 8th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pages 102-111, Alberta, Canada, 2002.
26. M. Boullé. Hands on pattern recognition, chapter Data grid models for preparation and modeling in supervised learning. Microtome, 2010.
27. C.P. Robert. The Bayesian Choice : From Decision Theoretic Foundations to Computational Implementation. Springer, 2007.
28. M. Boullé. Functional data clustering via piecewise constant nonparametric density estimation. Pattern Recognition, 45(12):4389-4401, 2012.
29. M. Abramowitz and I.A Stegun. Handbook of mathematical functions. Dover, New York, 1965.
30. R. Romain Guigourès, M Boullé, and F. Rossi. A Triclustering Approach for Time Evolving Graphs. In ICDM Workshops, 2012.
31. L. Candillier, I. Tellier, F. Torre, and O. Bousquet. Cascade evaluation of clustering algorithms. In Johannes Fürnkranz, Tobias Scheffer, and Myra Spiliopoulou, editors, 17th European Conference on Machine Learning (ECML'2006), volume LNAI 4212 of LNCS, pages 574-581, Berlin, Germany, september 2006. Springer Verlag.
32. T. Fawcett. Roc graphs: Notes and practical considerations for data mining researchers. T. Fawcett. ROC Graphs: Notes and Practical Considerations for Data Mining Researchers. Technical Report HPL-2003-4, HP Labs, 2003., 2003.
33. E. Keogh, Q. Zhu, B. Hu, Hao. Y., X. Xi, L. Wei, and C. A. Ratanamahatana. The UCR Time Series Classification/Clustering Homepage : www.cs.ucr.edu/-eamonn/time-series-data/, 2011.
34. J. Demšar. Statistical Comparisons of Classifiers over Multiple Data Sets. Journal of Machine Learning Research, 7:1-30, December 2006.