Time Series Data Mining: A Retail Application

Daniel Hebert, Billie Anderson, Alan Olinsky and J. Michael Hardin
Additional contact information
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

International Journal of Business Analytics (IJBAN), 2014, vol. 1, issue 4, 51-68

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.

Date: 2014
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