Real-time change detection in time series based on growing feature quantization

Proceedings of the International Joint Conference on Neural Networks

Volumn , Issue , 2012, Pages

  Kang, Yanfei ^a  

^a MONASH UNIVERSITY   (Australia)

Author keywords

Change Detection; Feature Space; Time Series; Vector Quantization

Indexed keywords

CHANGE DETECTION; CHANGE-POINTS; FEATURE SPACE; GREAT BRITAIN; HISTORICAL DATA; MODERATE RESOLUTION IMAGING SPECTRORADIOMETER; NORMALIZED DIFFERENCE VEGETATION INDEX DATUM; ONLINE METHODS; REAL-TIME CHANGES; REAL-TIME DETECTION; RIVER DISCHARGE; SIMULATED DATA; SIMULATED EVENTS; SLIDING WINDOW;

NEURAL NETWORKS; RADIOMETERS; SIGNAL DETECTION; TIME SERIES;

VECTOR QUANTIZATION;

EID: 84865079541     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/IJCNN.2012.6252381     Document Type: Conference Paper

References (35)

1. J. Verbesselt, R. Hyndman, G. Newnham, and D. Culvenor, "Detecting trend and seasonal changes in satellite image time series," Remote Sensing of Environment, vol. 114, no. 1, pp. 106 - 115, 2010. [Online]. Available: http://www.sciencedirect.com/science/article/pii/S003442570900265X
2. A. Zeileis, C. Kleiber, W. Krämer, and K. Hornik, "Testing and dating of structural changes in practice," Computational Statistics & Data Analysis, vol. 44, no. 1-2, pp. 109-123, Oct. 2003. [Online]. Available: http://www.sciencedirect.com/science/article/ B6V8V-485X6V4-6/1/ feb5424458e8a479d4ce3eb821cefc45
3. J. Bai and P. Perron, "Computation and analysis of multiple structural change models," Journal of Applied Econometrics, vol. 18, no. 1, pp. 1-22, 2003. [Online]. Available: http://dx.doi.org/10.1002/jae.659
4. J. Verbesselt, R. Hyndman, A. Zeileis, and D. Culvenor, "Phenological change detection while accounting for abrupt and gradual trends in satellite image time series," Remote Sensing of Environment, vol. 114, no. 12, pp. 2970 - 2980, 2010. [Online]. Available: http://www. sciencedirect.com/science/article/pii/S0034425710002336
5. M. Sharifzadeh, F. Azmoodeh, and C. Shahabi, "Change detection in time series data using wavelet footprints." in SSTD, ser. Lecture Notes in Computer Science, C. B. Medeiros, M. J. Egenhofer, and E. Bertino, Eds., vol. 3633. Springer, 2005, pp. 127-144. [Online]. Available: http://dblp.uni-trier. de/db/conf/ssd/sstd2005.html#SharifzadehAS05
6. D. Kifer, S. Ben-David, and J. Gehrke, "Detecting change in data streams," in VLDB 04: Proceedings of the 30th International Conference on Very Large Data Bases. Morgan Kaufmann Publishers Inc., 2004, pp. 180-191.
7. L. Firoiu and P. R. Cohen, "Segmenting time series with a hybrid neural networks - hidden markov model." in AAAI/IAAI, 2002, pp. 247-252. [Online]. Available: http://dblp.uni-trier.de/db/conf/aaai/aaai2002.html# FiroiuC02
8. E. J. Keogh, S. Chu, D. Hart, and M. J. Pazzani, "An online algorithm for segmenting time series." in ICDM, N. Cercone, T. Y. Lin, and X. Wu, Eds. IEEE Computer Society, 2001, pp. 289-296. [Online]. Available: http://dblp.uni-trier.de/db/conf/icdm/icdm2001.html#KeoghCHP01
9. C.-Y. Tsai and Y.-C. Shieh, "A change detection method for sequential patterns," Decision Support Systems, vol. 46, no. 2, pp. 501 - 511, 2009. [Online]. Available: http://www.sciencedirect.com/science/article/ pii/S0167923608001449
10. B. Salmon, J. Olivier, K. Wessels, W. Kleynhans, F. van den Bergh, and K. Steenkamp, "Unsupervised land cover change detection: Meaningful sequential time series analysis," Selected Topics in Applied Earth Observations and Remote Sensing, IEEE Journal of, vol. 4, no. 2, pp. 327 -335, june 2011.
11. R. S. Lunetta, J. F. Knight, J. Ediriwickrema, J. G. Lyon, and L. D. Worthy, "Land-cover change detection using multitemporal modis ndvi data," Remote Sensing of Environment, vol. 105, no. 2, pp. 142 - 154, 2006. [Online]. Available: http://www.sciencedirect.com/science/article/pii/ S0034425706002549
12. A. Culotta, "Detecting influenza outbreaks by analyzing twitter messages," CoRR, vol. abs/1007.4748, 2010. [Online]. Available: http://dblp.uni-trier.de/db/journals/corr/corr1007.html#abs-1007-4748
13. R. Fujimaki, T. Yairi, and K. Machida, "An approach to spacecraft anomaly detection problem using kernel feature space." in KDD, R. Grossman, R. Bayardo, and K. P. Bennett, Eds. ACM, 2005, pp. 401-410. [Online]. Available: http://dblp.uni-trier.de/db/conf/kdd/kdd2005.html#FujimakiYM05
14. P. Burge, J. Shawe-Taylor, C. Cooke, Y. Moreau, B. Preneel, and C. Stoermann, "Fraud detection and management in mobile telecommunications networks," in Security and Detection, 1997. ECOS 97., European Conference on, apr 1997, pp. 91 -96.
15. J. Jouini and M. Boutahar, "Evidence on structural changes in u.s. time series," Economic Modelling, vol. 22, no. 3, pp. 391 - 422, 2005. [Online]. Available: http://www.sciencedirect.com/science/article/pii/ S0264999304000483
16. R. Gray, "Vector quantization," ASSP Magazine, IEEE, vol. 1, no. 2, pp. 4 -29, april 1984.
17. Y. Kawahara and M. Sugiyama, "Change-point detection in time-series data by direct density-ratio estimation." in SDM. SIAM, 2009, pp. 389-400. [Online]. Available: http://dblp.uni-trier.de/db/conf/sdm/ sdm2009. html#KawaharaS09
18. E. J. Keogh and J. Lin, "Clustering of time-series subsequences is meaningless: implications for previous and future research." Knowl. Inf. Syst., vol. 8, no. 2, pp. 154-177, 2005. [Online]. Available: http://dblp.uni-trier.de/db/journals/kais/kais8.html#KeoghL05
19. J. R. Chen, "Making subsequence time series clustering meaningful." in ICDM. IEEE Computer Society, 2005, pp. 114-121. [Online]. Available: http://dblp.uni-trier.de/db/conf/icdm/icdm2005.html#Chen05
20. D. Q. Goldin, R. Mardales, and G. Nagy, "In search of meaning for time series subsequence clustering: matching algorithms based on a new distance measure." in CIKM, P. S. Yu, V. J. Tsotras, E. A. Fox, and B. Liu, Eds. ACM, 2006, pp. 347-356. [Online]. Available: http://dblp.uni-trier.de/db/conf/ cikm/cikm2006.html#GoldinMN06
21. X. Wang, K. A. Smith, and R. J. Hyndman, "Characteristicbased clustering for time series data." Data Min. Knowl. Discov., vol. 13, no. 3, pp. 335-364, 2006. [Online]. Available: http: //dblp.uni-trier.de/db/journals/ datamine/datamine13.html#WangSH06
22. E. Lughofer, "Extensions of vector quantization for incremental clustering." Pattern Recognition, vol. 41, no. 3, pp. 995-1011, 2008. [Online]. Available: http://dblp.uni-trier.de/db/journals/pr/pr41. html#Lughofer08
23. R. O. Duda, P. E. Hart, and D. G. Stork, Pattern Classification (2nd Edition), 2nd ed. Wiley-Interscience, Nov. 2001. [Online]. Available: http://www.amazon.com/exec/obidos/redirect?tag=citeulike07-20n&path=ASIN/ 0471056693
24. D. Alahakoon, S. Halgamuge, and B. Srinivasan, "Dynamic selforganizing maps with controlled growth for knowledge discovery," Neural Networks, IEEE Transactions on, vol. 11, no. 3, pp. 601 -614, may 2000.
25. A. Jirayusakul and S. Auwatanamongkol, "A supervised growing neural gas algorithm for cluster analysis." Int. J. Hybrid Intell. Syst., vol. 4, no. 2, pp. 129-141, 2007. [Online]. Available: http://dblp.uni-trier.de/db/ journals/ijhis/ijhis4.html#JirayusakulA07
26. I. Sledge and J. Keller, "Growing neural gas for temporal clustering," in Pattern Recognition, 2008. ICPR 2008. 19th International Conference on, dec. 2008, pp. 1 -4.
27. G. A. Carpenter and S. Grossberg, "Adaptive resonance theory." in Encyclopedia of Machine Learning, C. Sammut and G. I. Webb, Eds. Springer, 2010, pp. 22-35. [Online]. Available: http: //dblp.uni-trier.de/db/reference/ml/ ml2010.html#CarpenterG10
28. A.C. and Harvey, "The effects of seat belt legislation on british road casualities: A case study in structural modelling: A.c. harvey and j. durbing, journal of the royal statistical society, series a 149 (1986) (in press),"
29. International Journal of Forecasting, vol. 2, no. 4, pp. 496 - 497, 1986. [Online]. Available: http://www.sciencedirect.com/science/article/pii/ 016920708690097X
30. G. W. Cobb, "The problem of the nile: Conditional solution to a changepoint problem," Biometrika, vol. 65, no. 2, pp. pp. 243-251, 1978. [Online]. Available: http://www.jstor.org/stable/2335202
31. P. Jonsson and L. Eklundh, "Seasonality extraction by function fitting to time-series of satellite sensor data," Geoscience and Remote Sensing, IEEE Transactions on, vol. 40, no. 8, pp. 1824 - 1832, aug 2002.
32. E. Stanus, "Computerized sleep stages analysis," Signal Processing, vol. 10, no. 1, pp. 101-102, Jan. 1986. [Online]. Available: http://www.sciencedirect.com/science/article/B6V18-499905M-F/1/ 8dcc64245b115cbb2314b90f99a20b44
33. U. R. Acharya, E. C.-P. Chua, K. C. Chua, L. C. Min, and T. Tamura, "Analysis and automatic identification of sleep stages using higher order spectra." Int. J. Neural Syst., vol. 20, no. 6, pp. 509-521, 2010. [Online]. Available: http://dblp.uni-trier.de/db/journals/ijns/ijns20.html# AcharyaCCMT10
34. Y.-H. Lee, Y.-S. Chen, and L.-F. Chen, "Automated sleep staging using single eeg channel for rem sleep deprivation," in Bioinformatics and BioEngineering, 2009. BIBE '09. Ninth IEEE International Conference on, june 2009, pp. 439 -442.
35. S. Gnes, K. Polat, and S. Yosunkaya, "Efficient sleep stage recognition system based on eeg signal using k-means clustering based feature weighting." Expert Syst. Appl., vol. 37, no. 12, pp. 7922-7928, 2010. [Online]. Available: http://dblp.uni-trier.de/db/ journals/eswa/eswa37. html#GunesPY10a