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Mining Time Series Data

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Data Mining and Knowledge Discovery Handbook

Summary

Much of the world’s supply of data is in the form of time series. In the last decade, there has been an explosion of interest in mining time series data. A number of new algorithms have been introduced to classify, cluster, segment, index, discover rules, and detect anomalies/novelties in time series. While these many different techniques used to solve these problems use a multitude of different techniques, they all have one common factor; they require some high level representation of the data, rather than the original raw data. These high level representations are necessary as a feature extraction step, or simply to make the storage, transmission, and computation of massive dataset feasible. A multitude of representations have been proposed in the literature, including spectral transforms, wavelets transforms, piecewise polynomials, eigenfunctions, and symbolic mappings. This chapter gives a high-level survey of time series Data Mining tasks, with an emphasis on time series representations.

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Author information

Authors and Affiliations

  1. University of California, Riverside, USA

    Chotirat Ann Ratanamahatana, Jessica Lin, Dimitrios Gunopulos & Eamonn Keogh

  2. IBM T.J. Watson Research Center, Yorktown Heights, New-York, 10598, USA

    Michail Vlachos

  3. University of Texas, Arlington, USA

    Gautam Das

Authors
  1. Chotirat Ann Ratanamahatana
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  2. Jessica Lin
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  3. Dimitrios Gunopulos
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  4. Eamonn Keogh
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  5. Michail Vlachos
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  6. Gautam Das
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Editor information

Editors and Affiliations

  1. , Dept. Industrial Engineering, Tel Aviv University, Ramat Aviv, 69978, Israel

    Oded Maimon

  2. , Dept. Information Systems Engineering, Ben-Gurion University of the Negev, Beer-Sheva, 84105, Israel

    Lior Rokach

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Ratanamahatana, C.A., Lin, J., Gunopulos, D., Keogh, E., Vlachos, M., Das, G. (2009). Mining Time Series Data. In: Maimon, O., Rokach, L. (eds) Data Mining and Knowledge Discovery Handbook. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-09823-4_56

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