Table of Contents Author Guidelines Submit a Manuscript
Mathematical Problems in Engineering
Volume 2014 (2014), Article ID 879736, 14 pages
http://dx.doi.org/10.1155/2014/879736
Research Article

Time Series Outlier Detection Based on Sliding Window Prediction

College of Computer & Information, Hohai University, Nanjing 210098, China

Received 18 July 2014; Accepted 15 September 2014; Published 30 October 2014

Academic Editor: Jun Jiang

Copyright © 2014 Yufeng Yu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. J. D. Salas, “Analysis and modeling of hydrologic time series,” in Handbook of Hydrology, vol. 19, pp. 1–72, 1993. View at Google Scholar
  2. W. Gujer, Systems Analysis for Water Technology, Springer, Berlin, Germany, 2008. View at Publisher · View at Google Scholar
  3. N. Lauzon, Water resources data quality assessment and description of natural processes using artificial intelligence techniques [Ph.D. thesis], University of British Columbia, 2003.
  4. K. Yang and C. Shahabi, “A PCA-based similarity measure for multivariate time series,” in Proceedings of the 2nd ACM International Workshop on Multimedia Databases, pp. 65–74, ACM, November 2004. View at Scopus
  5. G. E. P. Box, G. M. Jenkins, and G. C. Reinsel, Time Series Analysis: Forecasting and Control, John Wiley & Sons, New York, NY, USA, 2013.
  6. D. Machiwal and M. K. Jha, Hydrologic Time Series Analysis: Theory and Practice, Springer, New York, NY, USA, 2012. View at Publisher · View at Google Scholar · View at MathSciNet
  7. D. M. Hawkins, Identification of Outliers, vol. 11, Chapman &Hall, London, UK, 1980. View at MathSciNet
  8. V. Chandola, A. Banerjee, and V. Kumar, “Anomaly detection: a survey,” ACM Computing Surveys, vol. 41, no. 3, article 15, 2009. View at Publisher · View at Google Scholar · View at Scopus
  9. M. Gupta, J. Gao, C. Aggarwal, and J. Han, Outlier Detection for Temporal Data, Synthesis Lectures on Data Mining and Knowledge Discovery, Morgan & Claypool, 2014.
  10. V. J. Hodge and J. Austin, “A survey of outlier detection methodologies,” Artificial Intelligence Review, vol. 22, no. 2, pp. 85–126, 2004. View at Publisher · View at Google Scholar · View at Scopus
  11. K. Das and J. Schneider, “Detecting anomalous records in categorical datasets,” in Proceedings of the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 220–229, ACM, August 2007. View at Publisher · View at Google Scholar · View at Scopus
  12. M. M. Breuniq, H.-P. Kriegel, R. T. Ng, and J. Sander, “LOF: identifying density-based local outliers,” ACM Sigmod Record, vol. 29, no. 2, pp. 93–104, 2000. View at Google Scholar · View at Scopus
  13. Z. He, X. Xu, and S. Deng, “Discovering cluster-based local outliers,” Pattern Recognition Letters, vol. 24, no. 9-10, pp. 1641–1650, 2003. View at Publisher · View at Google Scholar · View at Scopus
  14. C. C. Aggarwal and P. S. Yu, “Outlier detection with uncertain data,” in Proceedings of the 8th SIAM International Conference on Data Mining, pp. 483–493, April 2008. View at Scopus
  15. S. Ando, “Clustering needles in a haystack: An information theoretic analysis of minority and outlier detection,” in Proceedings of the 7th IEEE International Conference on Data Mining (ICDM '07), pp. 13–22, October 2007. View at Publisher · View at Google Scholar · View at Scopus
  16. A. Agovic, B. Arindam, G. Auroop, and P. Vladimir, “Anomaly detection using manifold embedding and its applications in transportation corridors,” Intelligent Data Analysis, vol. 13, no. 3, pp. 435–455, 2009. View at Publisher · View at Google Scholar · View at Scopus
  17. S. Barua and R. Alhajj, “A parallel multi-scale region outlier mining algorithm for meteorological data,” in Proceedings of the 15th ACM International Symposium on Advances in Geographic Information Systems (GIS '07), pp. 352–355, ACM, November 2007. View at Publisher · View at Google Scholar · View at Scopus
  18. F. Rasheed, P. Peng, R. Alhajj, and J. Rokne, “Fourier transform based spatial outlier mining,” in Intelligent Data Engineering and Automated Learning—IDEAL 2009, vol. 5788 of Lecture Notes in Computer Science, pp. 317–324, Springer, Berlin, Germany, 2009. View at Publisher · View at Google Scholar
  19. U. Rebbapragada, P. Protopapas, C. E. Brodley, and C. Alcock, “Finding anomalous periodic time series : an application to catalogs of periodic variable stars,” Machine Learning, vol. 74, no. 3, pp. 281–313, 2009. View at Publisher · View at Google Scholar · View at Scopus
  20. A. J. Fox, “Outliers in time series,” Journal of the Royal Statistical Society. Series B (Methodological), vol. 34, pp. 350–363, 1972. View at Google Scholar · View at MathSciNet
  21. J. Ma and S. Perkins, “Online novelty detection on temporal sequences,” in Proceedings of the 9th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD '03), pp. 613–618, ACM, August 2003. View at Publisher · View at Google Scholar · View at Scopus
  22. D. J. Hill and B. S. Minsker, “Anomaly detection in streaming environmental sensor data: a data-driven modeling approach,” Environmental Modelling and Software, vol. 25, no. 9, pp. 1014–1022, 2010. View at Publisher · View at Google Scholar · View at Scopus
  23. A. L. I. Oliveira and S. R. L. Meira, “Detecting novelties in time series through neural networks forecasting with robust confidence intervals,” Neurocomputing, vol. 70, no. 1–3, pp. 79–92, 2006. View at Publisher · View at Google Scholar · View at Scopus
  24. E. Keogh, J. Lin, A. W. Fu, and H. Van Herle, “Finding unusual medical time-series subsequences: algorithms and applications,” IEEE Transactions on Information Technology in Biomedicine, vol. 10, no. 3, pp. 429–439, 2006. View at Publisher · View at Google Scholar · View at Scopus
  25. E. Keogh, J. Lin, and A. Fu, “HOT SAX: efficiently finding the most unusual time series subsequence,” in Proceedings of the 5th IEEE International Conference on Data Mining (ICDM '05), pp. 226–233, Houston, Tex, USA, November 2005. View at Publisher · View at Google Scholar · View at Scopus
  26. L. Wei, E. Keogh, and X. Xi, “SAXually explicit images: finding unusual shapes,” in Proceedings of the 6th International Conference on Data Mining (ICDM '06), pp. 711–720, Hong Kong, December 2006. View at Publisher · View at Google Scholar · View at Scopus
  27. Y. Bu, T.-W. Leung, A. W.-C. Fu, E. J. Keogh, J. Pei, and S. Meshkin, “WAT: finding top-K discords in time series database,” in Proceedings of the 7th SIAM International Conference on Data Mining (SDM '07), pp. 449–454, April 2007. View at Scopus
  28. J. Lin, E. Keogh, A. Fu, and H. van Herle, “Approximations to magic: finding unusual medical time series,” in Proceedings of the 18th IEEE Symposium on Computer-Based Medical Systems, pp. 329–334, June 2005. View at Scopus
  29. M. Gupta, J. Gao, C. C. Aggarwal, and J. Han, “Outlier detection for temporal data: a survey,” IEEE Transactions on Knowledge and Data Engineering, vol. 26, no. 9, p. 1, 2014. View at Google Scholar
  30. Y. Zhang, N. A. S. Hamm, N. Meratnia, A. Stein, M. van de Voort, and P. J. M. Havinga, “Statistics-based outlier detection for wireless sensor networks,” International Journal of Geographical Information Science, vol. 26, no. 8, pp. 1373–1392, 2012. View at Publisher · View at Google Scholar · View at Scopus
  31. R. Frieda, I. Agueusopa, B. Bornkampb et al., “Bayesian outlier detection in INGARCH time series,” Sonderforschungsbereich (SFB) 823, 2012.
  32. A. Grané and H. Veiga, “Wavelet-based detection of outliers in financial time series,” Computational Statistics & Data Analysis, vol. 54, no. 11, pp. 2580–2593, 2010. View at Publisher · View at Google Scholar · View at Scopus
  33. C. Bilen and S. Huzurbazar, “Wavelet-based detection of outliers in time series,” Journal of Computational and Graphical Statistics, vol. 11, no. 2, pp. 311–327, 2002. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  34. F. Chebana and T. B. M. J. Ouarda, “Depth-based multivariate descriptive statistics with hydrological applications,” Journal of Geophysical Research: Atmospheres, vol. 116, no. D10, 2011. View at Publisher · View at Google Scholar · View at Scopus
  35. R. H. McCuen, Modeling Hydrologic Change: Statistical Methods, CRC Press, New York, NY, USA, 2002.
  36. Interagency Advisory Committee on Water Data, Guidelines for Determining Flood Flow Frequency: Bulletin 17B, U.S. Geological Survey, Office of Water Data Coordination, Reston, Va, USA, 1982.
  37. R. J. Hyndman and H. L. Shang, “Rainbow plots, bagplots, and boxplots for functional data,” Journal of Computational and Graphical Statistics, vol. 19, no. 1, pp. 29–45, 2010. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  38. F. Chebana, S. Dabo-Niang, and T. B. M. J. Ouarda, “Exploratory functional flood frequency analysis and outlier detection,” Water Resources Research, vol. 48, no. 4, Article ID W04514, 2012. View at Publisher · View at Google Scholar · View at Scopus
  39. W. W. Ng, U. S. Panu, and W. C. Lennox, “Chaos based Analytical techniques for daily extreme hydrological observations,” Journal of Hydrology, vol. 342, no. 1-2, pp. 17–41, 2007. View at Publisher · View at Google Scholar · View at Scopus
  40. J. M. Chambers, W. S. Cleveland, B. Kleiner, and P. A. Tukey, Graphical Methods for Data Analysis, Wadsworth, Belmont, Calif, USA, 1983.
  41. J. Han and M. Kamber, Data Mining: Concepts and Techniques, Morgan Kaufmann, San Francisco, Calif, USA, 2001.
  42. J. Ma and S. Perkins, “Time-series novelty detection using one-class support vector machines,” in Proceedings of the International Joint Conference on Neural Networks, vol. 3, pp. 1741–1745, July 2003. View at Publisher · View at Google Scholar · View at Scopus
  43. T. Fawcett, “An introduction to ROC analysis,” Pattern Recognition Letters, vol. 27, no. 8, pp. 861–874, 2006. View at Publisher · View at Google Scholar · View at Scopus