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Time Series Data Mining: A Retail Application

Author

Listed:
  • Daniel Hebert

    (Market Analyst, Rogers Corporation, Woodstock, CT, USA)

  • Billie Anderson

    (Department of Mathematics, Bryant University, Smithfield, RI, USA)

  • Alan Olinsky

    (Department of Mathematics, Bryant University, Smithfield, RI, USA)

  • J. Michael Hardin

    (Dean, Culverhouse College of Commerce and Business Administration, University of Alabama, Tuscaloosa, AL, USA)

Abstract

Modern technologies have allowed for the amassment of data at a rate never encountered before. Organizations are now able to routinely collect and process massive volumes of data. A plethora of regularly collected information can be ordered using an appropriate time interval. The data would thus be developed into a time series. Time series data mining methodology identifies commonalities between sets of time-ordered data. Time series data mining detects similar time series using a technique known as dynamic time warping (DTW). This research provides a practical application of time series data mining. A real-world data set was provided to the authors by dunnhumby. A time series data mining analysis is performed using retail grocery store chain data and results are provided.

Suggested Citation

  • Daniel Hebert & Billie Anderson & Alan Olinsky & J. Michael Hardin, 2014. "Time Series Data Mining: A Retail Application," International Journal of Business Analytics (IJBAN), IGI Global, vol. 1(4), pages 51-68, October.
  • Handle: RePEc:igg:jban00:v:1:y:2014:i:4:p:51-68
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    Cited by:

    1. Aversa, Joseph & Hernandez, Tony & Doherty, Sean, 2021. "Incorporating big data within retail organizations: A case study approach," Journal of Retailing and Consumer Services, Elsevier, vol. 60(C).

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