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Indexing and Mining Time Series Data

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Encyclopedia of GIS
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Synonyms

Distance Measures; Query-by-Content; Similarity Search; Spatio-temporal Indexing; Temporal Data; 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” (Meyer, 2005). 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...

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References

  • Aggarwal C, Hinneburg A, Keim DA (2001) On the surprising behavior of distance metrics in high dimensional space. In: Proceedings of the 8th international conference on database theory, London, 6 Jan 2001, pp 420–434

    Google Scholar 

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

    Article  Google Scholar 

  • Das G, Lin K, Mannila H, Renganathan G, Smyth P (1998) Rule discovery from time series. In: Proceedings of the 4th international conference on knowledge discovery and data mining, New York, 27–31 Aug 1998, pp 16–22

    Google Scholar 

  • Debregeas A, Hebrail G (1998) 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 Aug 1998, pp 179–183

    Google Scholar 

  • Faloutsos C, Ranganathan M, Manolopoulos Y (1994) 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 

  • Geurts P (2001) Pattern extraction for time series classification. In: Proceedings of principles of data mining and knowledge discovery, 5th European conference, Freiburg, 3–5 Sept 2001, pp 115–127

    Google Scholar 

  • Guralnik V, Srivastava J (1999) 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 Aug 1999, pp 33–42

    Google Scholar 

  • Indyk P, Koudas N, Muthukrishnan S (2000) Identifying representative trends in massive time series data sets using sketches. In: Proceedings of the 26th international conference on very large data bases, Cairo, 10–14 Sept 2000, pp 363–372

    Google Scholar 

  • Kahveci T, Singh A (2001) Variable length queries for time series data. In: Proceedings of the 17th international conference on data engineering, Heidelberg, 2–6 Apr 2001, pp 273–282

    Google Scholar 

  • Kalpakis K, Gada D, Puttagunta V (2001) Distance measures for effective clustering of ARIMA time-series. In: Proceedings of the IEEE international conference on data mining, San Jose, 29 Nov–2 Dec 2001, pp 273–280

    Google Scholar 

  • Keogh E, Kasetty S (2002) 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, 23–26 July 2002, pp 102–111

    Google Scholar 

  • Keogh E, Pazzani M (1998) 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 Aug 1998, pp 239–241

    Google Scholar 

  • Keogh E, Lonardi S, Chiu W (2002) 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, 23–26 July 2002, pp 550–556

    Google Scholar 

  • Meyer SC (2005) Analysis of base flow trends in urban streams, Northeastern Illinois, USA. Hydrogeol J 13:871–885

    Article  Google Scholar 

  • Popivanov I, Miller RJ (2002) Similarity search over time series data using wavelets. In: Proceedings of the 18th international conference on data engineering, San Jose, 26 Feb–1 Mar 2002, pp 212–221

    Google Scholar 

  • Rafiei D, Mendelzon AO (1998) Efficient retrieval of similar time sequences using DFT. In: Proceedings of the 5th international conference on foundations of data organization and algorithms, Kobe, 12–13 Nov 1998

    Google Scholar 

  • Shahabi C, Tian X, Zhao W (2000) 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, 26–28 July 2000, pp 55–68

    Google Scholar 

  • van Wijk JJ, van Selow E (1999) Cluster and calendar-based visualization of time series data. In: Proceedings 1999 IEEE symposium on information visualization, 25–26 Oct 1999. IEEE Computer Society, pp 4–9

    Google Scholar 

  • Wu Y, Agrawal D, El Abbadi A (2000) 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 Nov 2000, pp 488–495

    Google Scholar 

  • Xi X, Keogh EJ, Shelton CR, Li W, Ratanamahatana CA (2006) Fast time series classification using numerosity reduction. In: Cohen WW, Moore A (eds) Proceedings of the twenty-third international conference on machine learning (ICML 2006), Pittsburgh, 25–29 June 2006. ACM, pp 1033–1040

    Google Scholar 

  • Yi B, Faloutsos C (2000) Fast time sequence indexing for arbitrary LP norms. In: Proceedings of the 26th international conference on very large databases, Cairo, 10–14 Sept 2000, pp 385–394

    Google Scholar 

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Keogh, E. (2016). Indexing and Mining Time Series Data. In: Shekhar, S., Xiong, H., Zhou, X. (eds) Encyclopedia of GIS. Springer, Cham. https://doi.org/10.1007/978-3-319-23519-6_598-2

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  • DOI: https://doi.org/10.1007/978-3-319-23519-6_598-2

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  • Online ISBN: 978-3-319-23519-6

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