Skip to main content
SpringerLink
Log in
Book cover

International Symposium on Spatial and Temporal Databases

SSTD 2005: Advances in Spatial and Temporal Databases pp 127–144Cite as

Change Detection in Time Series Data Using Wavelet Footprints

  • Conference paper

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 3633))

Abstract

In this paper, we propose a novel approach to address the problem of change detection in time series data. Our approach is based on wavelet footprints proposed originally by the signal processing community for signal compression. We, however, exploit the properties of footprints to capture discontinuities in a signal. We show that transforming data using footprints generates nonzero coefficients only at the change points. Exploiting this property, we propose a change detection query processing scheme which employs footprint-transformed data to identify change points, their amplitudes, and degrees of change efficiently and accurately. Our analytical and empirical results show that our approach outperforms the best known change detection approach in terms of both performance and accuracy. Furthermore, unlike the state of the art approaches, our query response time is independent of the number of change points and the user-defined change threshold.

This is a preview of subscription content, log in via an institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Chakrabarti, K., Garofalakis, M.N., Rastogi, R., Shim, K.: Approximate query processing using wavelets. In: Abbadi, A.E., Brodie, M.L., Chakravarthy, S., Dayal, U., Kamel, N., Schlageter, G., Whang, K.-Y. (eds.) VLDB 2000, Proceedings of 26th International Conference on Very Large Data Bases, Cairo, Egypt, September 10-14, pp. 111–122. Morgan Kaufmann, San Francisco (2000)

    Google Scholar 

  2. Chen, Y., Dong, G., Han, J., Wah, B.W., Wang, J.: Multi-dimensional regression analysis of time-series data streams. In: VLDB, pp. 323–334 (2002)

    Google Scholar 

  3. Dragotti, P.L., Vetterli, M.: Wavelet transform footprints: Catching singularities for compression and denoising. In: ICIP 2000 (2000)

    Google Scholar 

  4. Dragotti, P.L., Vetterli, M.: Deconvolution with wavelet footprints for ill-posed inverse problems. In: IEEE Conference on Acoustics, Speech and Signal Processing, Orlando, Florida, USA, May 2002, vol. 2, pp. 1257–1260 (2002)

    Google Scholar 

  5. Dragotti, P.L., Vetterli, M.: Wavelet footprints: Theory, algorithms and applications. IEEE Transactions on Signal Processing 51(5), 1306–1323 (2003)

    Article  MathSciNet  Google Scholar 

  6. Firoiu, L., Cohen, P.R.: Segmenting time series with a hybrid neural networks - hidden markov model. In: Eighteenth national conference on Artificial intelligence. American Association for Artificial Intelligence, pp. 247–252 (2002)

    Google Scholar 

  7. Ge, X.: Segmental semi-markov models and applications to sequence analysis. PhD thesis, Chair-Padhraic Smyth (2002)

    Google Scholar 

  8. Guralnik, V., Srivastava, J.: Event detection from time series data. In: KDD 1999: Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining, pp. 33–42. ACM Press, New York (1999)

    Google Scholar 

  9. Jahangiri, M., Sacharidis, D., Shahabi, C.: SHIFT-SPLIT: I/O Efficient Maintenance of Wavelet-Transformed Multidimensional Data. In: Proceedings of the 24th ACM SIGMOD International Conference on Management of Data (2005)

    Google Scholar 

  10. Jahangiri, M., Shahabi, C.: ProDA: A Suit of WebServices for Progressive Data Analysis. In: Proceedings of 24th ACM SIGMOD International Conference on Management of Data (2005) (demostration)

    Google Scholar 

  11. Earlougher Jr., R.C.: Advances in Well Test Analysis. Society of Petroleum Engineers, vol. 5 (1977)

    Google Scholar 

  12. Keogh, E., Smyth, P.: A probabilistic approach to fast pattern matching in time series databases. In: Heckerman, D., Mannila, H., Pregibon, D., Uthurusamy, R. (eds.) Third International Conference on Knowledge Discovery and Data Mining, Newport Beach, CA, USA, pp. 24–30. AAAI Press, Menlo Park (1997)

    Google Scholar 

  13. Keogh, E.J., Chakrabarti, K., Mehrotra, S., Pazzani, M.J.: Locally adaptive dimensionality reduction for indexing large time series databases. In: SIGMOD Conference (2001)

    Google Scholar 

  14. Keogh, E.J., Chu, S., Hart, D., Pazzani, M.J.: An online algorithm for segmenting time series. In: ICDM 2001: Proceedings of the 2001 IEEE International Conference on Data Mining, pp. 289–296. IEEE Computer Society, Los Alamitos (2001)

    Chapter  Google Scholar 

  15. Lin, J., Keogh, E., Truppel, W.: Clustering of streaming time series is meaningless. In: DMKD 2003: Proceedings of the 8th ACM SIGMOD workshop on Research issues in data mining and knowledge discovery, pp. 56–65. ACM Press, New York (2003)

    Chapter  Google Scholar 

  16. Lin, J., Vlachos, M., Keogh, E.J., Gunopulos, D.: Iterative incremental clustering of time series. In: Bertino, E., Christodoulakis, S., Plexousakis, D., Christophides, V., Koubarakis, M., Böhm, K., Ferrari, E. (eds.) EDBT 2004. LNCS, vol. 2992, pp. 106–122. Springer, Heidelberg (2004)

    Chapter  Google Scholar 

  17. Mallat, S., Hwang, W.L.: Singularity detection and processing with wavelets. IEEE Trans. Inf. Th 38, 617–643 (1992)

    Article  MathSciNet  Google Scholar 

  18. Mallat, S., Zhang, Z.: Matching pursuits with time-frequency dictionaries. IEEE Transactions on Signal Processing 41(12), 3397–3415 (1993)

    Article  MATH  Google Scholar 

  19. Matias, Y., Vitter, J.S., Wang, M.: Wavelet-based histograms for selectivity estimation. In: SIGMOD 1998: Proceedings of the 1998 ACM SIGMOD international conference on Management of data, pp. 448–459. ACM Press, New York (1998)

    Chapter  Google Scholar 

  20. Oppenheim, A.V., Schafer, R.W.: Digital Signal Processing. Prentice Hall, Englewood Cliffs (1975)

    MATH  Google Scholar 

  21. Puttagunta, V., Kalpakis, K.: Adaptive methods for activity monitoring of streaming data. In: ICMLA, pp. 197–203 (2002)

    Google Scholar 

  22. Schmidt, R.R., Shahabi, C.: Propolyne: A fast wavelet-based algorithm for progressive evaluation of polynomial range-sum queries. In: Jensen, C.S., Jeffery, K., Pokorný, J., Šaltenis, S., Bertino, E., Böhm, K., Jarke, M. (eds.) EDBT 2002. LNCS, vol. 2287, pp. 664–681. Springer, Heidelberg (2002)

    Chapter  Google Scholar 

  23. Sharifzadeh, M., Azmoodeh, F., Shahabi, C.: Change detection in time series data using wavelet footprints. Technical Report 05-855, Department of Computer Science, University of Southern California (2005)

    Google Scholar 

  24. Yamanishi, K., ichi Takeuchi, J.: A unifying framework for detecting outliers and change points from non-stationary time series data. In: KDD 2002: Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining, pp. 676–681. ACM Press, New York (2002)

    Chapter  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Computer Science Department, University of Southern California, Los Angeles, CA, 90089-0781, USA

    Mehdi Sharifzadeh, Farnaz Azmoodeh & Cyrus Shahabi

Authors
  1. Mehdi Sharifzadeh
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Farnaz Azmoodeh
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Cyrus Shahabi
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Institute of Computing, CP 6176, University of Campinas, 13084-971, Campinas, Brazil

    Claudia Bauzer Medeiros

  2. National Center for Geographic Information and Analysis and Department of Spatial Information Science and Engineering, University of Maine, Boardman Hall, ME 04469-5711, Orono, USA

    Max J. Egenhofer

  3. Purdue University,  

    Elisa Bertino

Rights and permissions

Reprints and permissions

Copyright information

© 2005 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Sharifzadeh, M., Azmoodeh, F., Shahabi, C. (2005). Change Detection in Time Series Data Using Wavelet Footprints. In: Bauzer Medeiros, C., Egenhofer, M.J., Bertino, E. (eds) Advances in Spatial and Temporal Databases. SSTD 2005. Lecture Notes in Computer Science, vol 3633. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11535331_8

Download citation

  • DOI: https://doi.org/10.1007/11535331_8

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-28127-6

  • Online ISBN: 978-3-540-31904-7

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation