Skip to main content

Indexing and Mining Time Series Data

  • Reference work entry
Encyclopedia of GIS

Synonyms

Similarity search; Query-by-content; Distance measures; Temporal data; Spatio-temporal indexing; Temporal indexing

Definition

Time series data is ubiquitous; large volumes of time series data are routinely created in geological and meteorological domains. Although statisticians have worked with time series for more than a century, many of their techniques hold little utility for researchers working with massive time series databases (for reasons discussed below). There two major areas of research on time series databases, the efficient discovery of previously known patterns (indexing), and the discovery of previously unknown patterns (data mining). As a concrete example of the former a user may wish to “Find examples of a sudden increase, followed by slow decrease in lake volume anywhere in North America” [14]. Such a query could be expressed in natural language, however virtually all indexing systems assume the user will sketch a query shape. In contrast, data mining aims...

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

Access this chapter

Institutional subscriptions

Recommended Reading

  1. Aggarwal, C., Hinneburg, A., Keim, D.A.: On the surprising behavior of distance metrics in high dimensional space. In: Proceedings of the 8th International Conference on Database Theory, London, 6 January 2001, pp. 420–434

    Google Scholar 

  2. Chakrabarti, K., Keogh, E.J., Pazzani, M., Mehrotra, S.: Locally adaptive dimensionality reduction for indexing large time series databases. ACM Trans. Datab. Syst. 27(2), 188–228 (2002)

    Article  Google Scholar 

  3. Das, G., Lin, K., Mannila, H., Renganathan, G., Smyth, P.: Rule discovery from time series. In: Proceedings of the 4th International Conference on Knowledge Discovery and Data Mining, New York, 27–31 August 1998, pp. 16–22

    Google Scholar 

  4. Debregeas, A., Hebrail, G.: Interactive interpretation of kohonen maps applied to curves. In: Proceedings of the 4th International Conference of Knowledge Discovery and Data Mining, New York, 27–31 August 1998, pp. 179–183

    Google Scholar 

  5. Faloutsos, C., Ranganathan, M., Manolopoulos, Y.: Fast subsequence matching in time-series databases. In: Proceedings of the ACM SIGMOD International Conference on Management of Data, Minneapolis, 25–27 May 1994, pp. 419–429

    Google Scholar 

  6. Geurts, P.: Pattern extraction for time series classification. In: Proceedings of Principles of Data Mining and Knowledge Discovery, 5th European Conference, Freiburg, Germany, 3–5 September 2001, pp. 115–127

    Google Scholar 

  7. Guralnik, V., Srivastava, J.: Event detection from time series data. In: Proceedings of the 5th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Diego, 15–18 August 1999, pp. 33–42

    Google Scholar 

  8. Indyk, P., Koudas, N., Muthukrishnan, S.: Identifying representative trends in massive time series data sets using sketches. In: Proceedings of the 26th International Conference on Very Large Data Bases, Cairo, Egypt, 10–14 September 2000, pp. 363–372

    Google Scholar 

  9. Kahveci, T., Singh, A.: Variable length queries for time series data. In: Proceedings of the 17th International Conference on Data Engineering, Heidelberg, Germany, 2–6 April 2001, pp. 273–282

    Google Scholar 

  10. Kalpakis, K., Gada, D., Puttagunta, V.: Distance measures for effective clustering of ARIMA time-series. In: Proceedings of the IEEE International Conference on Data Mining, San Jose, 29 November–2 December 2001, pp. 273–280

    Google Scholar 

  11. Keogh, E., Lonardi, S., Chiu, W.: Finding Surprising Patterns in a Time Series Database In Linear Time and Space. In: The 8th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Edmonton, Alberta, Canada, 23–26 July 2002, pp. 550–556

    Google Scholar 

  12. Keogh, E., Pazzani, M.: An enhanced representation of time series which allows fast and accurate classification, clustering and relevance feedback. In: Proceedings of the 4th International Conference on Knowledge Discovery and Data Mining, New York, 27–31 August 1998, pp. 239–241

    Google Scholar 

  13. Keogh, E., Kasetty, S.: On the Need for Time Series Data Mining Benchmarks: A Survey and Empirical Demonstration. In: The 8th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Edmonton, Alberta, Canada, 23–26 July 2002, pp. 102–111

    Google Scholar 

  14. Meyer, S.C.: Analysis of base flow trends in urban streams, northeastern Illinois, USA. Hydrogeol. J. 13: 871–885 (2005)

    Article  Google Scholar 

  15. Popivanov, I., Miller, R.J.: Similarity search over time series data using wavelets. In: Proceedings of the 18th International Conference on Data Engineering, San Jose, 26 February–1 March 2002, pp. 212–221

    Google Scholar 

  16. Rafiei, D., Mendelzon, A.O.: Efficient retrieval of similar time sequences using DFT. In: Proceedings of the 5th International Conference on Foundations of Data Organization and Algorithms, Kobe, Japan, 12–13 November 1998

    Google Scholar 

  17. Shahabi, C., Tian, X., Zhao, W.: TSA‑tree: a wavelet based approach to improve the efficiency of multi-level surprise and trend queries. In: Proceedings of the 12th International Conference on Scientific and Statistical Database Management, Berlin, Germany, 26–28 July 2000, pp. 55–68

    Google Scholar 

  18. van Wijk, J.J., van Selow, E.: Cluster and calendar-based visualization of time series data. In: Proceedings 1999 IEEE Symposium on Information Visualization, 25–26 October 1999, pp 4–9. IEEE Computer Society

    Google Scholar 

  19. Wu, Y., Agrawal, D., El Abbadi, A.: A comparison of DFT and DWT based similarity search in time-series databases. In: Proceedings of the 9th ACM CIKM International Conference on Information and Knowledge Management. McLean, 6–11 November 2000, pp. 488–495

    Google Scholar 

  20. Xi, X., Keogh, E.J., Shelton, C.R., Li, W., Ratanamahatana, C.A.: Fast time series classification using numerosity reduction. In: Cohen, W.W., Moore, A. (eds.): Machine Learning, Proceedings of the Twenty-Third International Conference (ICML 2006), Pittsburgh, Pennsylvania, USA, 25–29 June 2006, pp. 1033–1040. ACM 2006

    Google Scholar 

  21. Yi, B., Faloutsos, C.: Fast time sequence indexing for arbitrary lp norms. In: Proceedings of the 26th International Conference on Very Large Databases, Cairo, Egypt, 10–14 September 2000, pp. 385–394

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2008 Springer-Verlag

About this entry

Cite this entry

Keogh, E. (2008). Indexing and Mining Time Series Data. In: Shekhar, S., Xiong, H. (eds) Encyclopedia of GIS. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-35973-1_598

Download citation

Publish with us

Policies and ethics